24-hour Physical Activity, Sedentary, and Sleep Profiles in Individuals with Cancer: A UK Biobank Cohort Study

24-hour Physical Activity, Sedentary, and Sleep Profiles in Individuals with Cancer: A UK Biobank Cohort Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search 24-hour Physical Activity, Sedentary, and Sleep Profiles in Individuals with Cancer: A UK Biobank Cohort Study View ORCID Profile Flora Le , View ORCID Profile Dorothea Dumuid , Olivia D’Elia , Alexander Haussmann , View ORCID Profile Emmanuel Stamatakis , View ORCID Profile Aiden Doherty , Yue Liao , View ORCID Profile Joshua F. Wiley doi: https://doi.org/10.1101/2025.02.25.25322841 Flora Le 1 School of Psychological Sciences, Monash University , Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Flora Le Dorothea Dumuid 2 Alliance for Research in Exercise, Nutrition and Activity, Allied Health & Human Performance, University of South Australia , Adelaide, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dorothea Dumuid Olivia D’Elia 1 School of Psychological Sciences, Monash University , Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander Haussmann 1 School of Psychological Sciences, Monash University , Victoria, Australia 3 Division of Physical Activity, Prevention, and Cancer, German Cancer Research Center , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emmanuel Stamatakis 4 School of Health Sciences, Faculty of Medicine and Health, University of Sydney , Camperdown, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emmanuel Stamatakis Aiden Doherty 5 Nuffield Department of Population Health, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aiden Doherty Yue Liao 6 Department of Kinesiology, University of Texas at Arlington , Arlington, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joshua F. Wiley 1 School of Psychological Sciences, Monash University , Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joshua F. Wiley For correspondence: Joshua.Wiley{at}monash.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background The 24h behaviour profile, including physical activity, sedentary time, and sleep, is disrupted following a cancer diagnosis and contributes to cancer-related outcomes. This study describes the 24h behaviour profiles of individuals with and without cancer. Methods Seven days of accelerometer data from the UK Biobank ( M±SD age = 62.3 ± 7.8y; 56.4% female) were derived by machine learning models to assess the 24h behaviour profile in individuals with cancer (n = 10 152; M±SD years since diagnosis = 7.4 ± 6.1y) compared to individuals without cancer at their accelerometry measurement (including individuals with other conditions [n = 66 403], and healthy [n = 14 726]). Diagnoses were identified through ICD-10 coding within cancer registries, hospital inpatient records, self-reports, primary care, and death registers. Bayesian compositional data analysis compared 24h behaviour profiles between individuals with and without cancer, considering time since cancer diagnosis and cancer type. Results The 24h behaviour profile of individuals with cancer consisted of 21.6 minutes in moderate-to-vigorous physical activity (MVPA), 303.5 minutes in light physical activity (LPA), 559.2 minutes in sedentary, and 555.7 minutes in sleep. Overall, they spent less time in MVPA -7.1 [-8.0, -6.1] min/day) and LPA -11.2 [-14.7, -7.7] min/day), accompanied by more sedentary time (+11.0 [7.2, 14.9] min/day) and sleep (+7.3 [4.6, 10.0] min/day), compared to healthy individuals. The least physically active profiles were observed within 1 year of cancer diagnoses and in individuals with blood, gastrointestinal tract, and lung cancer. Conclusions The 24h behaviour profiles differed by cancer history, time since diagnosis, and type. Specific behavioural trade-off strategies and support should be considered for cancer survivors, particularly in the year after diagnosis. Introduction Cancer is the second leading cause of death worldwide 1 , and annual incidence rates are projected to increase by 47% for the next two decades 2 . At the same time, an aging population and longer survival times are resulting in a growing population of cancer survivors 3 . Physical inactivity is a major risk factor for comorbidities 4 , cancer recurrence 5 , and cancer mortality 5 – 7 . However, individuals with cancer are reported to consistently engaged in lower physical activity level 8 than healthy individuals throughout all phases of treatment and stages of the illness 9 . Optimising physical activity is a main target of policies and guidelines for individuals with cancer 8 . However, physical activity should not be considered as an independent behaviour. Changing time in physical activity requires trade-offs with other behaviours within the 24h day. This is because each day consists of exhaustive and mutually exclusive behaviours that always sum to 24 hours, including moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary, and sleep. Among these behaviours, not only physical activity, but both longer sedentary time 10 and short sleep (<5h) 11 predict all-cause mortality in cancer survivors. Accordingly, the relative distribution of time across all these behaviours is more relevant than the absolute time spent in any single behaviour in isolation 12 – 14 . For example, an individual who reduces time in MVPA must compensate by spending more time in other behaviours (LPA, SB, and/or sleep period); they cannot decrease time in MVPA while keeping the time in other behaviours fixed. Understanding how individuals allocate their time across the full spectrum of 24h behaviours following a cancer diagnosis is crucial to the development of meaningful and targeted behaviour guidelines and interventions, that are feasible for this population and fit within the confines of a 24h day. Analysing 24h behaviours requires compositional data analysis (CoDA) 12 – 14 , a methodological approach that captures the relative information of 24h behaviours and allows them to simultaneously be in one model. To our best knowledge, no studies have characterised the time allocation across 24h behaviours in individuals with cancer using CoDA. In addition, existing guidelines on 24h behaviours have relied on self-reports, which could be limited by random error and systematic bias. Evidence from objective methods, such as accelerometers, can reduce misclassification of physical activity levels and better guide personalised interventions. This study leverages the large accelerometer data from the UK Biobank (UKB) study to systematically describe the 24h behaviour profile of individuals with and without cancer. We also evaluate how the 24h behaviour profile differs according to time since cancer diagnosis and cancer types. Methods Study design and participants The UKB is a population-based, prospective study conducted across the United Kingdom, with approximately 500 000 participants aged 40–69 years recruited in 2006–2010. We used data from a subset of 103 720 participants who provided accelerometer data in 2013–2015 15 . Raw accelerometer data from 103 626 participants were processed, excluding study withdrawals. Participants with device calibration or data reading errors (>1% of values outside +/−8g range), inadequate wear time (100 mg) were excluded. Participants with any incidences between accelerometer measurement and up to one year follow-up were excluded to account for reverse causality. Those with missing healthcare linkages or covariate data, were also excluded. Participant flow diagram is in Supplementary Table S2 . We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) 16 guideline (see Supplementary Figure S1 ). Cancer and non-cancer incidence ascertainment Cancer group Cancer incidence was identified using the cancer registries from England, Scotland, and Wales. We defined the Cancer group as those with one or more cancer incidences at the time of accelerometer measurement. Time since cancer diagnosis was calculated as time between the date of diagnosis and the first day of accelerometer measurement; individuals were categorised into three groups: 5 years since cancer diagnosis. The 14 Cancer types commonly occurred in the UKB were also identified (see Supplementary Table S2 for ICD-10 codes), including Blood, Breast, Colorectal, Endocrine Gland, Gastrointestinal, Genitourinary, Gynaecological, Head and Neck, Lung, Melanoma, Skin (non-melanoma), Prostate, Multiple Primary (more than one type), and Other Cancer . Participants with cancer who have one or more other conditions were included in the cancer group, due to high prevalence of multimorbidity with cancer (94% in the current study, consistent with 91% in a previous study 17 ). Non-cancer group Non-cancer incidence was identified using hospital inpatient records, self-reports, primary care, and death registers. The incidences were collated into 11 chronic conditions defined according to International Classification of Diseases (ICD-10) Chapters (see Supplementary Table S3 for ICD-10 codes). We defined two Non-cancer groups, 1) Healthy as no incidence, and 2) Other Conditions as one or more chronic conditions (except cancer) at the time of accelerometry. 24h behaviour assessment MVPA, LPA, sedentary, and sleep period were derived from the Axivity AX3 wrist-worn triaxial accelerometers. Participants were instructed to wear the device on their dominant wrist continuously for seven days. The accelerometers captured three-dimensional acceleration data at 100Hz with a dynamic range of ± 8g. Accelerometer data were extracted and processed using methods described previously 15 . Time spent in 24h behaviours were calculated using random forest and hidden Markov model machine-learning methods 18 . Statistical analysis We used a Bayesian compositional analysis approach. The raw 4-part composition of the 24h behaviours (MVPA, LPA, sedentary behaviour, and sleep period), were expressed as a set of three isometric log ratio ( ilr) coordinates for model estimation. This ilr transformation, using a sequential binary partition 19 , prevents the perfect multicollinearity associated with fitting raw compositional data. To determine the association between Time since cancer diagnosis and 24h behaviour profile, a Bayesian multivariate model (a) was fitted with the ilr coordinates representing the 24h behaviour profile as the outcome and Time since cancer diagnosis (5 categories consisting of 3 cancer groups [5 years], 2 non-cancer groups [ Other Conditions and Healthy ]) as the predictors. Time in 24h behaviours in the Cancer group was calculated as the weighted mean of the three cancer groups. Post-hoc contrasts were used to estimate the differences in 24h behaviours by Time since cancer diagnosis compared to the two non-cancer groups. Two methods of contrasts were employed 1) treatment versus control, where Time since cancer diagnosis was compared against the Healthy and Other Conditions groups, and 2) pairwise comparisons within Cancer group, a) <1 year versus 1-5 years; b) 5 years; and c) 1-5 years versus >5 years. To assess Cancer types and 24h behaviour profile, a Bayesian multivariate model (b) was fitted with the ilr coordinates representing the 24h behaviour profile as the outcome and Cancer types (16 categories of 14 cancer types, and 2 non-cancer groups) as the predictors. Post-hoc contrasts, using the treatment versus control method, estimated the difference in the time spent in 24h behaviours between each of the 14 cancer types and the two control groups ( Healthy and Other Conditions ), respectively. In line with previous studies 20 – 22 , model (a) adjusted for age at accelerometry, sex, ethnicity, Townsend deprivation index, education/qualification attainment, employment, smoking status, and alcohol consumption (see Supplementary Table S1 for directed acyclic graph). Model (b) adjusted for the same covariates plus time since cancer diagnosis as this may vary by cancer type. All continuous covariates were modelled using smooth functions to account for possible non-linear associations. Both models were fitted with weakly-informative priors and 4 chains with 2000 iterations including (500 warmups) per chain for a total of 6000 post-warmup draws. Model convergence was confirmed (all 400) 23 . Bayesian posterior means and 99% credible interval were reported. Analyses were performed in R using package multilevelcoda 24 , 25 and brms 26 . Results The analytical sample included 91 281 individuals [average age at accelerometer measurement (SD) = 62.30 (7.85) years; 56.4% female], details in Table 1 . There were 10 152 (11.1%) participants in the Cancer group, 66 403 (72.7%) in the Other Conditions group, and 14 726 (16.1%) in the Healthy group. Individuals with Cancer provided accelerometer data after 7.4 (SD = 6.1) years since cancer diagnosis. They had an average of 2.56 (1.80) other major chronic conditions by categories defined in the ICD-10, comparable with the Other Conditions group with 2.58 (1.53) conditions. Quantiles of 24h behaviour profile in individuals with cancer by demographic subgroups are reported in Supplementary Table S5 . View this table: View inline View popup Download powerpoint Table 1. Participant characteristics ( N = 91 352). 24h profiles of individuals with and without cancer Across the three groups, participants allocated most of their day in sedentary time, followed by sleep period, LPA, and lastly MVPA ( Figure 1 ). The 24h behaviour profile of individuals with Cancer consisted of 559.2 [99%CI 553.8, 564.4] minutes in sedentary behaviour, 555.7 [552.0, 559.5] minutes in sleep period, 303.5 [298.8, 308.5] minutes in LPA and 21.6 [20.6, 22.6] minutes in MVPA. Individuals with Other Conditions allocated 557.5 [553.0, 561.7] minutes in sedentary behaviour, 550.7 [547.6, 553.9] minutes in sleep period, 309.5 [305.5, 313.7] minutes in LPA and 22.3 [21.5, 23.2] minutes in MVPA, whereas Healthy individuals allocated 548.1 [543.1, 553.0] minutes in sedentary behaviour, 548.4 [545.1, 551.9] minutes in sleep period, 314.8 [310.2, 319.5] minutes in LPA and 28.6 [27.4, 29.9] minutes in MVPA. The estimated 24h behaviour profile of individuals with Cancer showed more similarity to that of individuals with Other Conditions than to that of individuals who were classified as Healthy . Download figure Open in new tab Figure 1. Estimated 24h behaviour profile (in minutes) across time since cancer diagnosis. Values are Bayesian posterior mean differences and 99% credible intervals. Model adjusted for age at accelerometer, sex, ethnicity, Townsend deprivation index, education/qualification attainment, employment, smoking status, and alcohol consumption. Vertical dashed lines represent estimated means, and shaded regions are 99% credible intervals, of non-cancer groups (green = healthy, grey = other conditions). a significant difference from Healthy , b significant difference from Other Conditions , c significant difference between 5 years since diagnosis, e significant difference between 5 years since diagnosis. Differences in 24h behaviour profiles across time since cancer diagnosis Throughout time since cancer diagnoses, individuals with Cancer showed significant differences in their 24h behaviours compared to Healthy individuals ( Table 2 and Figure 1 ). Specifically, individuals with Cancer spent less time in MVPA (-7.06 [99%CI -8.02, -6.09] minutes) and LPA (-11.24 [-14.74, -7.73] minutes) and more in sedentary time (+11.02 [7.19, 14.89] minutes) and sleep period (+7.28 [4.62, 9.95] minutes). The largest differences in 24h behaviour profiles were observed in individuals 5 year since cancer diagnosis had a 24h profile more similar to that of Healthy individuals. View this table: View inline View popup Download powerpoint Table 2. Estimated minute differences in 24h behaviour profile across time since cancer diagnosis. Values are Bayesian posterior mean differences and 99% credible intervals. Model adjusted for age at accelerometer, sex, ethnicity, Townsend deprivation index, education/qualification attainment, employment, smoking status, alcohol consumption, and other chronic conditions. MVPA = moderate-to-vigorous physical activity, LPA = light physical activity. Bolded values indicate 99% credible intervals not containing 0. For all Time since cancer diagnosis categories, individuals with Cancer allocated their time differently among MVPA, LPA, and sleep period, compared to individuals with Other Conditions , whereas their sedentary time was similar ( Table 2 and Figure 1 ). Specifically, individuals with Cancer spent on average, less time in MVPA (-0.76 [-1.42, -0.09] minutes) and LPA (-5.93 [-8.76, -3.1] minutes), and more time in sleep period (+5.01 [2.82, 7.14] minutes). The largest differences in 24h behaviour profiles were observed in individuals 5 year since cancer diagnosis showed a similar composition to that of individuals with Other Conditions, with the only difference found for sleep period (+2.86 [0.08, 5.59] minutes). Differences in 24h behaviours across cancer types Individuals with different Cancer types had different 24h composition compared to Non-cancer individuals, including those who are Healthy and those with Other Conditions ( Table 3 and Figure 2 ). Compared to Healthy individuals, all Cancer types were consistently associated with less time in MVPA, and several Cancer types were additionally associated with less time in LPA and more sedentary time. Compared to individuals with Other Conditions , several Cancer types were associated with less time in LPA, followed by MVPA, and more sedentary time. Only some Cancer types had different sleep periods compared to Non-cancer groups. Download figure Open in new tab Figure 2. Estimated 24h behaviour profile (in minutes) across cancer types. Values are Bayesian posterior mean differences and 99% credible intervals. Model adjusted for age at accelerometer, sex, ethnicity, Townsend deprivation index, education/qualification attainment, employment, smoking status, alcohol consumption, and time since cancer diagnosis. Vertical dashed lines represent estimated means, and shaded regions are 99% credible intervals, of non-cancer groups (green = healthy, grey = other conditions). a significant difference from Healthy , b significant difference from Other Conditions . View this table: View inline View popup Download powerpoint Table 3. Estimated minute differences in 24h behaviour profile in across cancer types. Values are Bayesian posterior mean differences and 99% credible intervals. Model adjusted for age at accelerometer, sex, ethnicity, Townsend deprivation index, education/qualification attainment, employment, smoking status, and alcohol consumption. MVPA = moderate-to-vigorous physical activity, LPA = light physical activity. Bolded values indicate 99% credible intervals not containing 0. Among Cancer types , individuals with Blood, Gastrointestinal Tract, and Lung Cancer showed the largest difference in their 24h behaviour profiles compared to Non-cancer individuals ( Table 3 and Figure 2 ). Compared to Healthy individuals, individuals with these three Cancer types spent less time in MPVA (estimated minute differences range between -12.58 [-15.11, -9.73] and -17.54 [-21.59, -12.54]) and LPA (estimated minute differences range between -33.99 [-48.66, -19.06] and -53.92 [-85.22, -20.57]). They also had more sedentary time (estimated minute differences range between +27.55 [11.62, 43.61] and +48.22 [11.03, 85.50]) and longer sleep period (estimated minute differences range between +19.01 [7.15, 30.88] and +23.24 [-3.49, 51.57]; statistically significant for Blood, not Gastrointestinal Tract, and Lung Cancer ). Similar patterns emerged in comparison to individuals with Other Conditions, but the magnitudes were smaller. Among Cancer types , individuals with Skin (non-melanoma), Prostate, and Melanoma Cancer showed the smallest difference in their 24h composition from Non-cancer individuals ( Table 3 and Figure 2 ). Despite having the smallest difference, these individuals had less time in MPVA (estimated minute differences range between -5.17 [-7.37, -3.19] and -7.25 [-10.67, -3.48]) and LPA (estimated minute differences range between -12.29 [-23.54, -1.94] and -20.37 [-36.45, -4.76]), and a longer sleep period (estimated minute differences range between +12.23 [3.63, 21.26] and +14.51 [2.45, 26.89]). Sedentary time was not different (estimated minute differences range between +5.63 [-5.83, 17.80] and +13.12 [-3.41, 29.83]). Similar patterns emerged in comparison to individuals with Other Conditions , but the estimates were smaller. Discussion In this large cohort study, we observed evidence of 24h behaviour disruption in individuals with Cancer . Overall, individuals with Cancer spent less time in MVPA and LPA, and compensated by more sedentary time and longer sleep periods, compared to Non-cancer individuals, including those are Healthy and those with Other Conditions . We also identified that the largest disruptions in 24h behaviours were observed in those within 1 year since diagnosis. For almost every Cancer type, the 24h behaviour profile deviated from that observed in Non-cancer , with individuals with Blood, Gastrointestinal Tract, and Lung Cancer showing the largest differences in their 24h behaviours compared to individuals without cancer. 24h behaviour profile in individuals with cancer vs non-cancer Findings highlight an overall profile of reduced physical activity regardless of intensity levels, and increased sedentary time, in individuals with Cancer compared to Non-cancer . Specifically, they had less MVPA (7 minutes less) and LPA (11 minutes less), and more sedentary time (+11 minutes) and sleep period (+7 minutes). On average, people with cancer had 22 minutes of MVPA per day. The adverse effects of the cancer disease and treatments, including fatigue, pain, and nausea, may limit the ability of individuals to achieve current recommendations 8 for cancer survivors, which focus on achieving the same levels of MVPA recommended for healthy individuals 27 . Our findings show that all activity behaviours are affected when people have a cancer diagnosis and suggest that a holistic approach which considers time use across the whole day is warranted. Such an approach may minimise sitting time in favour of physical activity across intensity levels for individuals with cancer, aligning with existing evidence on the reduced risk of cancer associated with engaging in vigorous intensity physical activity 22 , or MVPA and LPA at the expense of sedentary time 21 . For example, breaking up sedentary time with short bouts of physical activity at any intensity levels, could be a more feasible approach for individuals who are unable or reluctant to initiate or maintain adequate levels of MVPA. Individuals with Cancer also spent more time in sleep period than Non-cancer individuals. The average sleep period of individuals with cancer was 9.3 hours, exceeding the 7-9 hour threshold of optimal sleep recommended for healthy adults 28 . Sleep is important for improving quality of life 29 and mortality 30 of individuals with cancer. However, excessive sleep may be a consequence of cancer-related fatigue, and psychological distress (depression and anxiety), and risk factor for mortality 31 , 32 and incidence of other chronic conditions 32 . As sleep disturbances have been consistently reported throughout all phases of treatment and stages of cancer 33 – 35 , it is possible that our participants had a long sleep period but disrupted, unrestful sleep (e.g., longer sleep onset latency or night-time awakenings). Our sleep period variable only captured the time between sleep onset and offset, and did not consider night awakenings. To improve the understanding of sleep in cancer, further research should consider the multidimensional nature of sleep, including objective sleep duration and time awake in bed, and subjective sleep complaints. There is urgent need to optimally support sleep after cancer 36 as sleep difficulties persist well after diagnosis and completion of treatment 37 , 38 . 24h behaviour profile across time since cancer diagnosis Across the time since cancer diagnosis, the greatest deviation from the Healthy profile was observed in those within 1 year of cancer diagnosis. Individuals in this category engaged in 31 min less physical activity across intensity levels (9 minutes less in MVPA and 22 minutes less in LPA), and instead allocated the time to inactive/resting behaviours (17 minutes more sedentary time and 14 minutes more in sleep period), compared to Healthy individuals. Timely care for individuals recently diagnosed with cancer to meet physical activity guidelines might be a valuable approach to mitigate cancer-related physical and psychological symptoms 39 – 41 . Individuals 1-5 years since cancer diagnosis were more physically active than those within 1 year since diagnosis, and individuals 5 years or more after cancer diagnosis showed additional improvement. The gradual improvement in 24h behaviours along the trajectory of survivorship may be due to lifestyle modifications and cancer remission. Nevertheless, individuals in the 5 years since cancer diagnosis category were not as physically active as Non-cancer individuals, and their 24h behaviour profile still significantly deviated from the Healthy profile. This trajectory of the 24h behaviours underscores the value of cancer care aimed at 24h behaviours spanning from diagnosis to long-term survivorship to meet the needs of the growing population of cancer survivors 3 . 24h behaviour profile across cancer types Across cancer types, physical activity regardless of intensity levels was lower than in the Non-cancer group. Of the 14 cancer types examined, individuals with cancers that typically have a poorer prognosis (e.g., Blood, Gastrointestinal Tract, and Lung ), were the least active, whereas those with cancers that have better prognoses, including Skin (non-melanoma), Prostate, and Melanoma , were more active. For example, between 2013 and 2019, the 5-year relative survival rate for Lung cancer was 25%, compared to 97% for Prostate cancer 32 . These findings also suggest that specific 24h behaviour profiles across cancer types may be due to the severity of the cancer disease and invasiveness of treatment. Further research incorporating information on cancer stage and treatment could provide more systematic evidence. It is important to recognise that what is regarded as healthy might differ among individuals with cancer, especially those are diagnosed with invasive cancer and undergo aggressive treatments. For individuals with Blood, Gastrointestinal Tract, and Lung cancer, trying to achieve a physical activity guideline threshold may be difficult and have limited benefits on health outcomes and survival. A more feasible approach could be reducing sedentary time or optimising sleep. For example, psychological factors, such as depressive symptoms, intrusive and avoidant thoughts about cancer, have been found to predict long-term trajectory of sleep in cancer patients 37 . Future studies that promote the optimal dose of 24h behaviours, while addressing modifiable factors, could provide precise recommendations for specific cancer types. Strengths and limitations The strengths of this study include the use of Bayesian compositional data analysis to account for the compositional nature of 24h behaviours and obtain direct uncertainty estimates, while accommodating potential non-linear associations and adjustment for several plausible confounders. The large sample size enables comparison across the progression of cancer disease, and several common and underrepresented cancer types. The wearables-based measures of behaviours overcome inherent limitations of self-report and provide useful evidence to enhance the current public guidelines on 24h behaviours. The use of wearables to track 24h behaviour trajectories holds promise to facilitate better awareness and management of cancer-related symptoms. Despite the temporal sequence of the prospective design, the observational design of the UKB limits our analysis from probing causality or the within-person changes in 24 behaviour time allocation from pre-diagnosis to post-diagnosis. This study is also limited by the lack of information on stage and grade of the tumour, and treatment, from the UKB. Further, we did not exclude those with other conditions from the Cancer group, given the high prevalence of multimorbidity (94%) in the Cancer group. Given the cancer’s clinical complexity, it is difficult to precisely disentangle whether the other chronic conditions are contributors or consequences of cancer, and how they uniquely contribute to the 24h behaviour profile. The potential misclassification of posture and movement, which is inherent to wrist-worn devices, limits our ability to separate the effects of sleep duration versus time awake in bed, which is important for the understanding of sleep and informing care. Lastly, survival bias may have influenced the UKB data and our observations. Further studies in nationally representative cohorts should evaluate the generalisability. Conclusion This study provided insights into 24h behaviour profiles among individuals with cancer, across time since cancer diagnosis and cancer types, and how they differ to profiles observed among individuals without cancer. Our study can improve progress toward optimal survivorship care delivery for the large and growing population of individuals living with and beyond cancer. Public guidelines for healthy individuals have transitioned from providing guidance for independent behaviours and towards integrating 24h behaviours 42 , but only general MVPA recommendations are available for cancer survivors 8 . Our findings are supportive of an approach towards healthy daily balance of physical activity, sedentary, and sleep, rather than one-size-fits-all target of increasing physical activity 43 for individuals with cancer. Recommendations on 24h behaviours should be implemented ideally following a cancer diagnosis and follow-up into long-term survivorship, and tailored to cancer types. People with cancers that have poor prognoses, including Blood , Lung , and Gastrointestinal cancers, had the least healthy 24h behaviour profile, highlighting the need for further support and resources to be allocated to optimise survivorship care and health outcomes for these groups. Ethics approval The UK Biobank has generic ethical approval from the Northwest Multi-Centre Research Ethics Committee (11/NW/03820). Data availability Participants who accepted the invitation to join the UK Biobank cohort provided written, informed consent and were given reimbursement for travel expenses. Data are available on application to the UK Biobank ( www.ukbiobank.ac.uk/ ). This research is approved for access under application #62254. Disclosure Statement Financial Disclosure: none. Non-financial Disclosure: none. Code availability Analysis code for this study is available at https://github.com/florale/ukb-cancer-24h Supplementary Materials View this table: View inline View popup Supplementary Table S1. STROBE Statement—Checklist of items that should be included in reports of cohort studies View this table: View inline View popup Download powerpoint Supplementary Table S2. ICD-10 codes for Cancer. View this table: View inline View popup Supplementary Table S3. ICD-10 Codes For Other Chronic Conditions. View this table: View inline View popup Download powerpoint Supplementary Table S4. UK Biobank variables used in the study. View this table: View inline View popup Download powerpoint Supplementary Table S5. 24h behaviour profiles (in raw minutes) in individuals with cancer ( N = 10 152). View this table: View inline View popup Download powerpoint Supplementary Table S6. Unadjusted estimated minute differences in 24h behaviour profile across time since cancer diagnosis. Values are Bayesian posterior mean differences and 99% credible intervals. MVPA = moderate-to-vigorous physical activity, LPA = light physical activity. Bolded values indicate 99% credible intervals not containing 0. View this table: View inline View popup Download powerpoint Supplementary Table S7. Unadjusted estimated minute differences in 24h behaviour profile across cancer types. Values are Bayesian posterior mean differences and 99% credible intervals. MVPA = moderate-to-vigorous physical activity, LPA = light physical activity. Bolded values indicate 99% credible intervals not containing 0. Download figure Open in new tab Supplementary Figure S1. Directed acyclic graph. Download figure Open in new tab Supplementary Figure S2. Participant flow diagram for data analysis. Download figure Open in new tab Supplementary Figure S3. Unadjusted estimated 24h behaviour profile (in minutes) across time since cancer diagnosis. Values are Bayesian posterior mean differences and 99% credible intervals. Vertical dashed lines represent estimated means, and shaded regions are 99% credible intervals, of non-cancer groups (green = healthy, grey = other conditions). a significant difference from Healthy , b significant difference from Other Conditions , c significant difference between 5 years since diagnosis, e significant difference between 5 years since diagnosis. Download figure Open in new tab Supplementary Figure S4. Unadjusted estimated 24h behaviour profile (in minutes) across cancer types. Values are Bayesian posterior mean differences and 99% credible intervals. Vertical dashed lines represent estimated means, and shaded regions are 99% credible intervals, of non-cancer groups (green = healthy, grey = other conditions). a significant difference from Healthy , b significant difference from Other Conditions . Acknowledgement We thank the participants in the UK Biobank and the UK Biobank for making the data and resources available. FL was supported by Monash University through a Monash Graduate Scholarship and a Monash International Tuition Scholarship. DD was funded by an Australian Research Council (ARC) Discovery Early Career Award (DECRA) DE230101174 and by the Centre of Research Excellence in Driving Global Investment in Adolescent Health funded by National Health and Medical Research Council (NHMRC) GNT1171981. AH was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation [540707655]). JFW was supported by a NHMRC fellowship (1178487). is supported by the Wellcome Trust [223100/Z/21/Z, 227093/Z/23/Z], Novo Nordisk, Swiss Re, and the British Heart Foundation Centre of Research Excellence [RE/18/3/34214]. YL is supported by the American Institute for Cancer Research [IIG 849385] and the American Heart Association [23CDA1050513] References 1. ↵ Kocarnik JM , Compton K , Dean FE , et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019 . JAMA oncology . 2022 ; 8 ( 3 ): 420 – 444 . OpenUrl CrossRef PubMed 2. ↵ Sung H , Ferlay J , Siegel RL , et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries . CA: a cancer journal for clinicians . 2021 ; 71 ( 3 ): 209 - 249 . OpenUrl CrossRef PubMed 3. ↵ Tonorezos E , Devasia T , Mariotto AB , et al. Prevalence of cancer survivors in the United States . JNCI: Journal of the National Cancer Institute . 2024 ; 116 ( 11 ): 1784 – 1790 . OpenUrl PubMed 4. ↵ Jung W , Cho IY , Jung J , et al. Changes in physical activity and cardiovascular disease risk in cancer survivors: a nationwide cohort study . Cardio Oncology . 2024 ; 6 ( 6 ): 879 – 889 . OpenUrl 5. ↵ Cormie P , Zopf EM , Zhang X , Schmitz KH . The impact of exercise on cancer mortality, recurrence, and treatment-related adverse effects . Epidemiologic reviews . 2017 ; 39 ( 1 ): 71 – 92 . OpenUrl CrossRef PubMed 6. Friedenreich CM , Stone CR , Cheung WY , Hayes SC . Physical activity and mortality in cancer survivors: a systematic review and meta-analysis . JNCI cancer spectrum . 2020 ; 4 ( 1 ): pkz080 . OpenUrl 7. ↵ Benke IN , Leitzmann M , Behrens G , Schmid D . Physical activity in relation to risk of prostate cancer: a systematic review and meta-analysis . Annals of Oncology . 2018 ; 29 ( 5 ): 1154 – 1179 . OpenUrl PubMed 8. ↵ Rock CL , Thomson CA , Sullivan KR , et al. American Cancer Society nutrition and physical activity guideline for cancer survivors . CA: a cancer journal for clinicians . 2022 ; 72 ( 3 ): 230 – 262 . OpenUrl CrossRef PubMed 9. ↵ Cao C , Patel AV , Liu R , Cao Y , Friedenreich CM , Yang L. Trends and cancer-specific patterns of physical activity, sleep duration, and daily sitting time among US cancer survivors, 1997-2018 . JNCI: Journal of the National Cancer Institute . 2023 ; 115 ( 12 ): 1563 – 1575 . OpenUrl PubMed 10. ↵ Cao C , Friedenreich CM , Yang L . Association of daily sitting time and leisure-time physical activity with survival among US cancer survivors . JAMA oncology . 2022 ; 8 ( 3 ): 395 – 403 . OpenUrl PubMed 11. ↵ Xiao Q , Arem H , Pfeiffer R , Matthews C . Prediagnosis sleep duration, napping, and mortality among colorectal cancer survivors in a large US cohort . Sleep . 2017 ; 40 ( 4 ): zsx010 . OpenUrl PubMed 12. ↵ Dumuid D , Stanford TE , Martin-Fernández J-A , et al. Compositional data analysis for physical activity, sedentary time and sleep research . Statistical methods in medical research . 2018 ; 27 ( 12 ): 3726 – 3738 . OpenUrl CrossRef PubMed 13. Dumuid D , Pedišić Ž , Palarea-Albaladejo J , Martín-Fernández JA , Hron K , Olds T. Compositional data analysis in time-use epidemiology: what, why, how . International journal of environmental research and public health . 2020 ; 17 ( 7 ): 2220 . OpenUrl 14. ↵ Dumuid D , Pedišić Ž , Stanford TE , et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour . Statistical methods in medical research . 2019 ; 28 ( 3 ): 846 – 857 . OpenUrl CrossRef PubMed 15. ↵ Doherty A , Jackson D , Hammerla N , et al. Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study . PloS one . 2017 ; 12 ( 2 ): e0169649 . OpenUrl CrossRef PubMed 16. ↵ Von Elm E , Altman DG , Egger M , et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies . International journal of surgery . 2014 ; 12 ( 12 ): 1495 – 1499 . OpenUrl PubMed 17. ↵ Koné AP , Scharf D . Prevalence of multimorbidity in adults with cancer, and associated health service utilization in Ontario, Canada: a population-based retrospective cohort study . BMC cancer . 2021 ; 21 : 1 – 13 . OpenUrl CrossRef PubMed 18. ↵ Walmsley R , Chan S , Smith-Byrne K , et al. Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease . British journal of sports medicine . 2022 ; 56 ( 18 ): 1008 – 1017 . OpenUrl Abstract / FREE Full Text 19. ↵ Egozcue JJ , Pawlowsky-Glahn V , Mateu-Figueras G , Barcelo-Vidal C . Isometric logratio transformations for compositional data analysis . Mathematical geology . 2003 ; 35 ( 3 ): 279 – 300 . OpenUrl CrossRef GeoRef Web of Science 20. ↵ Barker J , Smith Byrne K , Doherty A , et al. Physical activity of UK adults with chronic disease: cross-sectional analysis of accelerometer-measured physical activity in 96 706 UK Biobank participants . International Journal of Epidemiology . 2019 ; 48 ( 4 ): 1167 – 1174 . OpenUrl CrossRef PubMed 21. ↵ Shreves MAH , Small SR , Walmsley R , et al. Amount and intensity of physical activity and risk of incident cancer in the UK Biobank . medRxiv . 2023 ; 22. ↵ Stamatakis E , Ahmadi MN , Friedenreich CM , et al. Vigorous intermittent lifestyle physical activity and cancer incidence among nonexercising adults: The UK Biobank Accelerometry Study . JAMA oncology . 2023 ; 9 ( 9 ): 1255 – 1259 . OpenUrl PubMed 23. ↵ Vehtari A , Gelman A , Simpson D , Carpenter B , Bürkner P-C . Rank-normalization, folding, and localization: An improved R ^ for assessing convergence of MCMC (with discussion) . Bayesian analysis . 2021 ; 16 ( 2 ): 667 – 718 . OpenUrl 24. ↵ Le F , Dumuid D , Stanford TE , Wiley JF. Bayesian multilevel compositional data analysis with the R package multilevelcoda . arXiv preprint arXiv: 241112407 . 2024 ; 25. ↵ Le F , Stanford TE , Dumuid D , Wiley JF . Bayesian multilevel compositional data analysis: introduction, evaluation, and application . arXiv preprint arXiv: 240503985 . 2024 ; 26. ↵ Bürkner P-C. brms: An R package for Bayesian multilevel models using Stan . Journal of statistical software . 2017 ; 80 : 1 – 28 . OpenUrl 27. ↵ Bull FC , Al-Ansari SS , Biddle S , et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour . British journal of sports medicine . 2020 ; 54 ( 24 ): 1451 – 1462 . OpenUrl Abstract / FREE Full Text 28. ↵ Consensus Conference Panel , Watson NF , Badr MS , et al. Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society . Journal of Clinical Sleep Medicine . 2015 ; 11 ( 6 ): 591 – 592 . OpenUrl PubMed 29. ↵ Fortner BV , Stepanski EJ , Wang SC , Kasprowicz S , Durrence HH . Sleep and quality of life in breast cancer patients . Journal of pain and symptom management . 2002 ; 24 ( 5 ): 471 – 480 . OpenUrl CrossRef PubMed Web of Science 30. ↵ Collins KP , Geller DA , Antoni M , et al. Sleep duration is associated with survival in advanced cancer patients . Sleep medicine . 2017 ; 32 : 208 – 212 . OpenUrl PubMed 31. ↵ Cappuccio FP , D’Elia L , Strazzullo P , Miller MA . Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies . Sleep . 2010 ; 33 ( 5 ): 585 – 592 . OpenUrl CrossRef PubMed Web of Science 32. ↵ Zheng NS , Annis J , Master H , et al. Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program . Nature Medicine . 2024 /07/19 2024; doi: 10.1038/s41591-024-03155-8 OpenUrl CrossRef 33. ↵ Büttner-Teleagă A , Kim Y-T , Osel T , Richter K . Sleep disorders in cancer—a systematic review . International journal of environmental research and public health . 2021 ; 18 ( 21 ): 11696 . OpenUrl 34. Kairaitis K , Madut AS , Subramanian H , et al. Cancer sleep symptom-related phenotypic clustering differs across three cancer specific patient cohorts . Journal of Sleep Research . 2022 ; 31 ( 5 ): e13588 . OpenUrl PubMed 35. ↵ Al Maqbali M , Al Sinani M , Alsayed A , Gleason AM . Prevalence of sleep disturbance in patients with cancer: a systematic review and meta-analysis . Clinical nursing research . 2022 ; 31 ( 6 ): 1107 – 1123 . OpenUrl PubMed 36. ↵ Wiley JF. The urgency of supportive care for sleep after cancer . Oxford University Press US ; 2023 . p. zsad120 . 37. ↵ Chan J , Ng DWL , Liao Q , et al. Trajectories of sleep disturbance in cancer survivors during the first 2 years post-treatment . Sleep . 2023 ; 46 ( 8 ): zsad052 . OpenUrl PubMed 38. ↵ Bean HR , Diggens J , Ftanou M , Weihs KL , Stanton AL , Wiley JF . Insomnia and fatigue symptom trajectories in breast cancer: a longitudinal cohort study . Behavioral Sleep Medicine . 2021 ; 19 ( 6 ): 814 – 827 . OpenUrl PubMed 39. ↵ Belloni S , Arrigoni C , Caruso R . Effects from physical exercise on reduced cancer-related fatigue: a systematic review of systematic reviews and meta-analysis . Acta Oncologica . 2021 ; 60 ( 12 ): 1678 – 1687 . OpenUrl 40. Takemura N , Cheung DST , Smith R , et al. Effectiveness of aerobic exercise and mind-body exercise in cancer patients with poor sleep quality: a systematic review and meta-analysis of randomized controlled trials . Sleep Medicine Reviews . 2020 ; 53 : 101334 . OpenUrl PubMed 41. ↵ Tavares VDdO , Cuthbert C , Teychenne M , et al. The effects of exercise on anxiety and depression in adults with cancer: A meta-review of meta-analyses . Journal of Psychosocial Oncology . 2024 : 1 – 24 . 42. ↵ Ross R , Chaput J-P , Giangregorio LM , et al. Canadian 24-hour movement guidelines for adults aged 18–64 years and adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep . Applied physiology, nutrition, and metabolism . 2020 ; 45 ( 10 ): S57 – S102 . OpenUrl 43. ↵ Dumuid D , Olds T , Sawyer SM . Moving beyond more: Towards a healthy balance of daily behaviours . The Lancet . 2021 ; 398 ( 10298 ): 373 – 374 . OpenUrl View the discussion thread. Back to top Previous Next Posted February 25, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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