# Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study

Mathilde Chen, Benjamin Landré, Pedro Marques-Vidal, Vincent T van Hees, April C.E. van Gennip, Mikaela Bloomberg, Manasa S Yerramalla, Mohamed

Amine benadjaoud, Séverine Sabia

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Mathilde Chen, Benjamin Landré, Pedro Marques-Vidal, Vincent T van Hees, April C.E. van Gennip, et al.. Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study. EClinicalMedicine, 2023, 55, pp.101773. 10.1016/j.eclinm.2022.101773. irsn-03953929v2

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# Identification of physical activity and sedentary behaviour dimensions that predict mortality risk in older adults: Development of a machine learning model in the Whitehall II accelerometer sub-study and external validation in the CoLaus study

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Mathilde Chen,ª,h,\* Benjamin Landré,ª,h Pedro Marques-Vidal,ᵇ Vincent T. van Hees,ᶜ April C. E. van Gennip,ᵈ,ᵉ Mikaela Bloomberg,ᵉ Manasa S. Yerramalla,ª Mohamed Amine Benadjaoud,ᵇ and Séverine Sabiaª,f

oa

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## Summary

**Background** Identification of new physical activity (PA) and sedentary behaviour (SB) features relevant for health at older age is important to diversify PA targets in guidelines, as older adults rarely adhere to current recommendations focusing on total duration. We aimed to identify accelerometer-derived dimensions of movement behaviours that predict mortality risk in older populations.

**Methods** We used data on 21 accelerometer-derived features of daily movement behaviours in 3991 participants of the UK-based Whitehall II accelerometer sub-study (25.8% women, 60–83 years, follow-up: 2012–2013 to 2021, mean = 8.3 years). A machine-learning procedure was used to identify core PA and SB features predicting mortality risk and derive a composite score. We estimated the added predictive value of the score compared to traditional sociodemographic, behavioural, and health-related risk factors. External validation in the Switzerland-based CoLaus study (N = 1329, 56.7% women, 60–86 years, follow-up: 2014–2017 to 2021, mean = 3.8 years) was conducted.

**Findings** In total, 11 features related to overall activity level, intensity distribution, bout duration, frequency, and total duration of PA and SB, were identified as predictors of mortality in older adults and included in a composite score. Both in the derivation and validation cohorts, the score was associated with mortality (hazard ratio = 1.10 (95% confidence interval = 1.05–1.15) and 1.18 (1.10–1.26), respectively) and improved the predictive value of a model including traditional risk factors (increase in C-index = 0.007 (0.002–0.014) and 0.029 (0.002–0.055), respectively).

**Interpretation** The identified accelerometer-derived PA and SB features, beyond the currently recommended total duration, might be useful for screening of older adults at higher mortality risk and for diversifying PA and SB targets in older populations whose adherence to current guidelines is low.

**Funding** National Institute on Aging; UK Medical Research Council; British Heart Foundation; Wellcome Trust; French National Research Agency; GlaxoSmithKline; Lausanne Faculty of Biology and Medicine; Swiss National Science Foundation.

**Copyright** © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** Physical activity; Mortality; Accelerometer; Prediction; Older adults

eClinicalMedicine
2023;55: 101773

Published Online xxx
https://doi.org/10.
1016/j.eclinm.2022.
101773

<footnote>ªUniversité Paris Cité, Inserm U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Avenue de Verdun, 75010, Paris, France</footnote>

<footnote>ᵇDepartment of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Switzerland</footnote>

<footnote>ᶜAccelting, Almere, the Netherlands</footnote>

<footnote>ᵈDepartment of Internal Medicine, Maastricht University Medical Centre, the Netherlands</footnote>

<footnote>ᵉSchool for Cardiovascular Diseases CARIM, Maastricht University, the Netherlands</footnote>

<footnote>ᵉDepartment of Epidemiology and Public Health, University College London, UK</footnote>

<footnote n="9">⁹Institute for Radiological Protection and Nuclear Safety (IRSN), Fontenay-Aux-Roses, France</footnote>

<footnote>\*Corresponding author.
E-mail address: mathilde.chen@inserm.fr (M. Chen).</footnote>

<footnote>ᵇThese authors contributed equally.</footnote>
Research in context

Evidence before this study

We searched PubMed for studies aiming at the identification of physical activity and sedentary behaviour features predicting mortality risk in older adults indexed in MEDLINE from January 1, 2000 to September 19, 2022, written in English or French, using the following search terms: (("mortality" OR "death") AND ("physical activity" OR "exercise" OR "sedentary" OR "accelerometry") AND (predict*) AND ("aged" OR "older")). In the four identified relevant studies, accelerometer-derived features were reported as strong predictors of old-age mortality, however there was no clear consensus on their relative importance due to differing methodologies and a limited consideration of the multidimensional nature of movement behaviours. In addition, despite considerable heterogeneity in the older adult population, previous studies have neither examined the internal validity of their prediction models in subgroups, nor the external validity, precluding generalisation of results beyond the study population.

Added value of this study

Using a machine learning approach adapted to correlated predictors, we identified 11 features related to overall activity level, intensity distribution, and total duration, bouts duration, and frequency of physical activity and sedentary behaviour bouts as relevant predictors of 8-year mortality in a cohort of UK-based older adults. Composite score derived from these features showed good predictive ability in subgroups defined by age, sex, body mass index, and morbidity status. In addition, findings were validated in an independent Switzerland-based cohort.

Implications of all the available evidence

Adherence to current physical activity guidelines focussing mainly on the total duration in moderate-to-vigorous physical activity is low among older adults. The present study identified several dimensions of movement behaviours, in addition to total duration in physical activity, that predict mortality risk in older adults, above and beyond traditional risk factors. It might be relevant to integrate these dimensions into future physical activity guidelines.

Introduction

Physical activity (PA) is an important determinant of healthy aging,¹ with substantial evidence showing its protective role against morbidity and death.² Emerging evidence also suggests that sedentary behaviour (SB) might be deleterious for health,³ leading to recent guidelines promoting reduction of time in SB in addition to practise of moderate to vigorous physical activity (MVPA).⁴⁻⁶ However, most of the evidence is primarily based on self-reported measures of movement behaviours.⁷ Although informative, these approaches cannot capture all dimensions of PA and SB, such as overall activity level, total and bouts duration, frequency, timing, and intensity distribution throughout the day.⁸ Accelerometers allow the measurement of multiple features that provide an in-depth characterisation of these dimensions. Furthermore, adherence to current PA and SB guidelines among older adults is relatively low⁹,¹⁰ with evidence accumulating that PA messages focusing on the total duration of PA may be perceived as unreachable goals and less efficient than tailored approaches.¹¹,¹² Identifying additional dimensions of PA and SB, collectively referred to as movement behaviours, relevant for health might benefit the population if they were perceived as easier to adhere to than increased time in MVPA.

To date, the few prospective studies that have eval-
uated the effectiveness and added value of objectively-
assessed PA and SB features in predicting mortality

among older adults suggest that some of these features
(e.g. activity volume and fragmentation—the propensity
to interrupt an activity bout) are among the strongest
predictors of all-cause mortality.¹³⁻¹⁶ However, these
studies focused only on a limited set of PA and SB
features, either investigated separately and/or without
considering their correlated structure. Furthermore,
whereas the older adult population is known to be
heterogenous, neither internal validation in different
subgroups of age, sex, or health condition nor external
validation in an independent cohort has been conduct-
ed, limiting the generalisability of existing findings.

To address these limitations, the objective of this
study was to identify PA and SB features that are core
predictors of mortality risk among 21 accelerometer-
assessed features in a large cohort of older adults. The
added predictive value of the selected PA and SB fea-
tures as compared to other known traditional risk fac-
tors was evaluated both in subgroups defined by age,
sex, body mass index (BMI), or morbidity status, as well
as in an independent external cohort.

Methods

Study participants

The Whitehall II study is an ongoing prospective cohort
study established in 1985–1988 among 10,308 British
civil servants¹⁷ with clinical examinations every four–five
years since inception. An accelerometer measure was
added to the 2012–2013 wave of data collection for
participants seen at the London clinic and those living in
the south-eastern regions of England who underwent
clinical examination at home. This study followed the
reporting guidelines for prediction model and develop-
ment (TRIPOD).

PA and SB features

Participants were requested to wear a tri-axial acceler-
ometer (GENEActiv Original; Activinsights Ltd,
Kimbolton, UK) on their non-dominant wrist for nine
consecutive 24-h days. Accelerometer data, sampled at
85.7 Hz and expressed relative to gravity (1 g = 9.81 m/s²),
were processed using GGIR R package (version 2.3-3).¹⁸
Euclidean Norm of raw accelerations Minus One with
negative numbers rounded to zero was calculated¹⁹ and
corrected for calibration error and non-wear time.¹⁸ Sleep
periods were detected using a validated algorithm guided
by a sleep log.²⁰ Average acceleration over 60-s epochs <40
milligravity (mg) during waking period was classified as
SB, 40–99 mg as light-intensity PA (LIPA), and ≥100 mg
as MVPA.¹⁸,²¹

We searched the most commonly used movement
behaviour features in the literature that could be
extracted from accelerometer data using the GGIR R
package and identified 21 features covering six di-
mensions of daily movement behaviours. Overall activity
level was measured using average acceleration. Total
duration was investigated using total time in SB, LIPA,
and MVPA. Bouts duration was measured using mean
duration of sedentary/LIPA/MVPA bout (i.e. uninter-
rupted episode), time accumulated in sedentary bouts
lasting <10 min, 10–30 min, or ≥30 min, and time
accumulated in LIPA/MVPA bouts lasting < or
≥10 min. Frequency was characterized by the number of
sedentary/LIPA/MVPA bouts and the number of days
with ≥30 min of MVPA.²² Timing of PA was defined as
the start of the most active 5 consecutive hours during
the waking period. Intensity distribution was character-
ized by the intercept and the gradient of the linear
relationship between intensity and time accumulated in
that intensity.²¹ More information on these features is
provided in Supplementary Table S1.

Mortality

Deaths from any cause were ascertained using mortality
records drawn from the UK Office for National Statistics
Mortality Register until 28th February 2021. The tracing
exercise was carried out using the unique National
Health Service identification number of each partici-
pant. The start of follow-up was the screening date at the
2012–2013 study wave, and participants were censored
at the date of death or end of the follow-up, whichever
came first.

Covariates

Covariates were selected to represent traditional risk
factors for mortality and were drawn from question-
naires and clinical evaluations conducted in 2012–2013,
as well as from electronic health records. They included:
socio-demographic factors (age, sex, ethnicity, marital
status, and education level), *behavioural* (smoking status,
alcohol consumption, and fruit and vegetable con-
sumption) and *health-related* factors (BMI, hypertension,
hyperlipidemia, diabetes, number of limited basic ac-
tivities of daily living (ADL), number of limited instru-
mental ADL (IADL), and number of chronic diseases
among coronary heart disease, stroke, heart failure,
arthritis, cancer, depression, dementia, Parkinson’s
disease, and chronic obstructive pulmonary disease)
(see *Supplementary Methods 1* for details).

Statistical analyses

Three sets of analyses were conducted and are described
in following paragraphs: 1) development of PA and SB
composite scores that predict mortality risk in the
Whitehall accelerometer sub-study; 2) internal valida-
tion of the composite scores; and 3) external validation.

Development of PA and SB composite scores that
predict mortality risk

In exploratory analysis we first examined the association
of each of the 21 PA and SB features with mortality, in
separate models, using a Cox regression model adjusted
for traditional risk factors (socio-demographic, behav-
ioural, and health-related factors). Then, we identified
among these 21 features the ones most relevant for
mortality prediction using sparse Partial Least Square
regression for censored data, a machine learning
method adapted to highly correlated features.²³,²⁴ This
method jointly performs predictor selection and
dimension reduction by deriving composite scores
composed of the most relevant features, which are
tuned by cross-validation (Supplementary Methods 2).
The two composite scores resulting from this analysis,
referred to as composite score 1 and composite score 2,
represented the best set of accelerometer-derived pre-
dictors of all-cause mortality.

Internal validation

The association between the composite scores and
mortality was examined using hazard ratio (HR) derived
from Cox regression. First, Model 1 included only
traditional risk factors. Then, in Model 2, composite
score 1 was added to Model 1 and finally, in Model 3,
composite score 2 was further included in Model 2.
Potential non-linear associations of composite scores
with mortality were tested using likelihood ratio test
comparing models with only a linear term against
models with cubic spline terms. As no evidence of a
non-linear relationship of composite scores with mor-
tality was found (p = 0.82 and 0.23, respectively for
composite scores 1 and 2), composite scores were
entered as linear terms in all analyses.

Predictive accuracy was assessed using: Royston's
modified R², a measure of the overall prediction per-
formance of the model; Akaike information criterion
(AIC), an estimator of a model's prediction error
considering its complexity; sensitivity and specificity as
measures of classification accuracy (the optimal cut-off
was established by maximizing the Youden index);
and Harrell's C-statistic for survival models to measure
discrimination. With the exception of AIC, higher
values indicate better model performance. To evaluate
the added predictive performance of composite scores 1
and 2 compared to traditional risk factors, the AIC and
C-statistic of Models 2 and 3 were compared with Model
1 as the reference. Additionally, Model 3 was compared
with Model 2 as reference to estimate the gain in pre-
dictive performance of composite score 2 as compared
to composite score 1. Associated confidence intervals
were computed using 1000 bootstrap replications. In the
absence of an association with mortality in the fully-
adjusted model (Model 3) and performance gain of
composite score 2 compared to composite score 1,
further validation analyses were conducted using com-
posite score 1 only.

In sensitivity analysis, we examined the potential
impact of the duration of the waking period on our
findings by repeating the analysis using PA and SB
features standardized to the duration of the waking
period (as described in Supplementary Table S1) before
inclusion in the selection procedure.

Previous studies suggested that the association be-
tween movement behaviours and mortality may differ
according to different risk factors, including age, sex,
BMI, and morbidity status.²⁵,²⁶ Accordingly, we exam-
ined the association of composite score 1 with mortality
risk as well as its added predictive value in the following
subgroups: (i) age <74 and ≥74 years old (to allow
enough mortality cases in each group); (ii) men and
women, (iii) participants with normal BMI (<25 kg/m²),
overweight (BMI 25–29.9 kg/m²), and obesity (BMI
≥30 kg/m²); and (iv) presence and absence of morbidity
(defined as one or more chronic disease(s)).

External validation

To assess generalisability, the association of the retained
composite score 1 with mortality and its added predic-
tive value were also evaluated in 1648 participants aged
≥60 years from the CoLaus accelerometer sub-study
(Lausanne, Switzerland).²⁷ Socio-demographic, behav-
ioural, health-related, and accelerometer variables were
extracted from questionnaires and clinical evaluations at
the 2014–2017 study wave (see details in Supplementary
Methods 3).

At each wave, Whitehall II participants provided
informed written consent. All participants from the
CoLaus study gave their signed informed consent before
entering the study. Research ethics approval was ob-
tained from the National Health Service London—
Harrow Research Ethic Committee (latest reference
number 85/0938) for the Whitehall II study and from
the Ethics Commission of Canton Vaud for CoLaus
study (239/09, decision of 21st June 2021).

All analyses were undertaken using R version 4.1.2
(http://www.r-project.org) with a two-sided p < 0.05
considered statistically significant. All relevant R scripts
and documentation can be accessed via the project
repository (https://github.com/MathildeChen/PA-SB-
dimensions-mortality-Whitehall). Technical informa-
tion regarding raw accelerometer data processing can be
found in the GGIR repository (https://github.com/
wadpac/GGIR).

Role of the funding source

The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report. The first author had full access to
the datasets and all authors approved the final manu-
script for submission.

Results

Among the 6308 participants in the 2012–2013 wave of
data collection in Whitehall II, 4880 were invited to
participate in the accelerometer sub-study, of whom
4008 had valid accelerometer data (Supplementary
Figure S1). Excluding those with missing covariates
led to an analytical sample of 3991 (mean age: 69.4
years, 25.8% women). Over a mean follow-up of 8.1
(standard deviation [SD] = 1.3) years, 410 deaths were
recorded. Deceased participants were more likely to be
older, married/cohabitating, less educated, have a worse
cardiovascular risk profile, more comorbidities, and
more functional limitations compared to those who
survived over the follow-up (Table 1). Participants who
died spent more time in SB, which was accumulated in
longer, uninterrupted episodes, and less time in higher
activity intensities compared to surviving participants.
Later timing of PA was observed among surviving par-
ticipants (Table 2). Differences for each of these features
were also evident in separate analyses adjusted for socio-
demographic, behavioural, and health-related factors
with the exception of mean duration of LIPA bouts
(Hazard Ratio (HR) for 1-SD increment [95% confi-
dence interval (CI)]: 0.94 [0.86, 1.04], p = 0.22), time in
≥10 min LIPA bouts (HR [95% CI]: 0.93 [0.84, 1.03],
p = 0.16), and timing of PA (HR [95% CI]: 0.96 (0.87,
1.06), p = 0.41) (Table 2). Most of accelerometer-derived
features exhibited strong correlation, with the exception
of timing of PA (absolute coefficient ≤ 0.05)
<table><thead><tr><th rowspan="2"></th><th colspan="2">All-cause mortality</th><th rowspan="2">p-value</th></tr><tr><th>No (N = 3581)</th><th>Yes (N = 410)</th></tr></thead><tbody><tr><td colspan="4"><strong>Socio-demographic factors</strong></td></tr><tr><td>Age (years), M (SD)</td><td>68.9 (5.5)</td><td>73.8 (5.3)</td><td>&lt;0.001</td></tr><tr><td>Women</td><td>938 (26.2%)</td><td>92 (22.4%)</td><td>0.10</td></tr><tr><td>Non-white ethnicity</td><td>259 (7.2%)</td><td>36 (8.8%)</td><td>0.26</td></tr><tr><td>Education</td><td></td><td></td><td></td></tr><tr><td>No academic qualifications</td><td>321 (9.0%)</td><td>49 (12.0%)</td><td>&lt;0.001</td></tr><tr><td>Lower secondary school</td><td>1128 (31.5%)</td><td>144 (35.1%)</td><td></td></tr><tr><td>Higher secondary school</td><td>984 (27.5%)</td><td>127 (31.0%)</td><td></td></tr><tr><td>University</td><td>843 (23.5%)</td><td>72 (17.6%)</td><td></td></tr><tr><td>Higher degree</td><td>305 (8.5%)</td><td>18 (4.4%)</td><td></td></tr><tr><td>Not married/cohabitating</td><td>873 (24.4%)</td><td>137 (33.4%)</td><td>&lt;0.001</td></tr><tr><td colspan="4"><strong>Behavioural factors</strong></td></tr><tr><td>Smoking status</td><td></td><td></td><td></td></tr><tr><td>Never smoker</td><td>1787 (49.9%)</td><td>174 (42.4%)</td><td>0.017</td></tr><tr><td>Past smoker</td><td>1599 (44.7%)</td><td>210 (51.2%)</td><td></td></tr><tr><td>Current smoker</td><td>195 (5.4%)</td><td>26 (6.3%)</td><td></td></tr><tr><td>Alcohol intake</td><td></td><td></td><td></td></tr><tr><td>0 unit/week</td><td>702 (19.6%)</td><td>110 (26.8%)</td><td>0.003</td></tr><tr><td>1-14 units/week</td><td>2039 (56.9%)</td><td>215 (52.4%)</td><td></td></tr><tr><td>&gt;14 units/week</td><td>840 (23.5%)</td><td>85 (20.7%)</td><td></td></tr><tr><td>Fruit and vegetable intake</td><td></td><td></td><td></td></tr><tr><td>Less than daily</td><td>718 (20.1%)</td><td>108 (26.3%)</td><td>0.001</td></tr><tr><td>Daily</td><td>752 (21.0%)</td><td>100 (24.4%)</td><td></td></tr><tr><td>Twice daily or more</td><td>2111 (59.0%)</td><td>202 (49.3%)</td><td></td></tr><tr><td colspan="4"><strong>Health-related factors</strong></td></tr><tr><td>Body mass index</td><td></td><td></td><td></td></tr><tr><td>Normal (&lt;25 kg/m²)</td><td>1383 (38.6%)</td><td>165 (40.2%)</td><td>0.80</td></tr><tr><td>Overweight (25-29.9 kg/m²)</td><td>1549 (43.3%)</td><td>171 (41.7%)</td><td></td></tr><tr><td>Obesity (≥30 kg/m²)</td><td>649 (18.1%)</td><td>74 (18.0%)</td><td></td></tr><tr><td>Hypertension</td><td>1804 (50.4%)</td><td>262 (63.9%)</td><td>&lt;0.001</td></tr><tr><td>Hyperlipidemia</td><td>1792 (50.0%)</td><td>229 (55.9%)</td><td>0.026</td></tr><tr><td>Diabetes</td><td>434 (12.1%)</td><td>80 (19.5%)</td><td>&lt;0.001</td></tr><tr><td>Number of chronic diseases,<sup>a</sup> M (SD)</td><td>0.51 (0.72)</td><td>0.84 (0.93)</td><td>&lt;0.001</td></tr><tr><td>Number of basic ADL,<sup>b</sup> M (SD)</td><td>0.15 (0.59)</td><td>0.47 (1.13)</td><td>&lt;0.001</td></tr><tr><td>Number of IADL,<sup>c</sup> M (SD)</td><td>0.18 (0.58)</td><td>0.40 (0.90)</td><td>&lt;0.001</td></tr></tbody><tfoot><tr><td colspan="4">Data are N (%), otherwise specified. Abbreviations: SD: standard deviation; ADL: activities of daily living; IADL: instrumental activities of daily living. <sup>a</sup>Chronic disease include coronary heart disease, stroke, heart failure, arthritis, cancer, depression, dementia, Parkinson's disease, and chronic obstructive pulmonary disease. <sup>b</sup>Basic ADL include difficulty in dressing, walking, bathing, eating, getting in bed, and using the toilet. <sup>c</sup>IADL include difficulty in cooking, shopping for groceries, making telephone calls, taking medication, doing housework, and managing money.</td></tr><tr><td colspan="4"><strong>Table 1:</strong> Sample characteristics in 2012-2013 by mortality status at the end of the follow-up (February 2021) in the Whitehall II accelerometer sub-study.</td></tr></tfoot></table>

(Supplementary Figure S2), leading us not to mutually adjust models for all features.

## Identified PA and SB features

Of the 21 PA and SB features investigated, 12 related to overall activity, total duration, bouts duration, frequency, intensity distribution, and timing of PA were selected and computed as two composite scores by the machine learning procedure (Fig. 1). The selected features substantially contributed to composite score 1 with similar magnitude (absolute loads from 0.20 to 0.35), with the

exception of timing of PA (–0.08). Higher composite score 1 represented lower overall activity level, less time in MVPA, accumulation of SB time in longer bouts, less frequent bouts of LIPA and MVPA, less days complying the recommended daily practise of 30 min of MVPA, more time in low intensity activity (reflected by higher intensity distribution intercept), and less well distributed activities of different intensities. In contrast, timing of PA contributed the most to composite score 2, followed by time in sedentary bouts of 10–30 min and average acceleration. In sensitivity analysis using PA and SB features standardized according to waking
<table><thead><tr><th rowspan="2"></th><th rowspan="2">Overall (N = 3991)</th><th colspan="2">All-cause mortality</th><th rowspan="2">p-value<sup>a</sup></th><th colspan="2">Association with mortality<sup>b</sup></th></tr><tr><th>No (N = 3581)</th><th>Yes (N = 410)</th><th>HR (95% CI)</th><th>p-value</th></tr></thead><tbody><tr><td colspan="8"><strong>Overall activity level</strong></td></tr><tr><td>Mean acceleration (mg)</td><td>31.8 (9.7)</td><td>32.3 (9.7)</td><td>27.2 (8.4)</td><td>&lt;0.001</td><td>0.76 (0.67, 0.87)</td><td>&lt;0.001</td></tr><tr><td colspan="8"><strong>Total duration (min/day)</strong></td></tr><tr><td>SB</td><td>717.9 (100.1)</td><td>713.0 (98.5)</td><td>760.5 (104.6)</td><td>&lt;0.001</td><td>1.22 (1.10, 1.35)</td><td>&lt;0.001</td></tr><tr><td>LIPA</td><td>210.3 (69.1)</td><td>213.0 (67.6)</td><td>186.9 (77.0)</td><td>&lt;0.001</td><td>0.86 (0.78, 0.95)</td><td>0.0027</td></tr><tr><td>MVPA</td><td>56.1 (38.5)</td><td>58.2 (38.6)</td><td>37.3 (33.0)</td><td>&lt;0.001</td><td>0.74 (0.64, 0.85)</td><td>&lt;0.001</td></tr><tr><td colspan="8"><strong>Bouts duration</strong></td></tr><tr><td>Mean duration (min) of activity bout of</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SB</td><td>11.5 (5.9)</td><td>11.1 (5.2)</td><td>14.3 (9.8)</td><td>&lt;0.001</td><td>1.07 (1.02, 1.14)</td><td>0.011</td></tr><tr><td>LIPA</td><td>2.4 (0.4)</td><td>2.4 (0.4)</td><td>2.4 (0.4)</td><td>0.118</td><td>0.94 (0.86, 1.04)</td><td>0.22</td></tr><tr><td>MVPA</td><td>2.3 (0.9)</td><td>2.3 (0.9)</td><td>2.0 (1.1)</td><td>&lt;0.001</td><td>0.87 (0.77, 0.99)</td><td>0.03</td></tr><tr><td>Time (min/day) in &lt;10 min bouts of</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SB</td><td>147.1 (41.0)</td><td>148.6 (40.1)</td><td>134.6 (45.8)</td><td>&lt;0.001</td><td>0.89 (0.81, 0.98)</td><td>0.023</td></tr><tr><td>LIPA</td><td>184.0 (54.1)</td><td>186.4 (52.7)</td><td>162.9 (61.2)</td><td>&lt;0.001</td><td>0.85 (0.77, 0.93)</td><td>0.001</td></tr><tr><td>MVPA</td><td>43.4 (27.6)</td><td>45.0 (27.5)</td><td>30.0 (24.7)</td><td>&lt;0.001</td><td>0.75 (0.65, 0.86)</td><td>&lt;0.001</td></tr><tr><td>Time (min/day) in ≥10 min bouts of</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LIPA</td><td>26.4 (21.3)</td><td>26.6 (21.2)</td><td>24.0 (21.6)</td><td>0.02</td><td>0.93 (0.84, 1.03)</td><td>0.16</td></tr><tr><td>MVPA</td><td>12.7 (17.2)</td><td>13.3 (17.5)</td><td>7.3 (12.5)</td><td>&lt;0.001</td><td>0.84 (0.72, 0.98)</td><td>0.023</td></tr><tr><td>Time (min/day) in 10-29.9 min bouts of</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SB</td><td>185.7 (45.3)</td><td>187.4 (44.4)</td><td>171.0 (50.1)</td><td>&lt;0.001</td><td>0.90 (0.82, 0.99)</td><td>0.031</td></tr><tr><td>Time (min/day) in ≥30 min bouts of</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SB</td><td>385.0 (143.0)</td><td>377.0 (138.3)</td><td>454.9 (163.8)</td><td>&lt;0.001</td><td>1.22 (1.11, 1.34)</td><td>&lt;0.001</td></tr><tr><td colspan="8"><strong>Frequency</strong></td></tr><tr><td>Number (N/day) of bouts of</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SB</td><td>71.8 (16.0)</td><td>72.5 (15.5)</td><td>66.2 (18.6)</td><td>&lt;0.001</td><td>0.87 (0.80, 0.96)</td><td>0.005</td></tr><tr><td>LIPA</td><td>86.0 (21.6)</td><td>87.1 (21.0)</td><td>76.3 (24.4)</td><td>&lt;0.001</td><td>0.83 (0.75, 0.92)</td><td>&lt;0.001</td></tr><tr><td>MVPA</td><td>23.2 (13.6)</td><td>24.0 (13.4)</td><td>16.6 (12.7)</td><td>&lt;0.001</td><td>0.76 (0.67, 0.87)</td><td>&lt;0.001</td></tr><tr><td>Number of days with ≥30 min of MVPA</td><td>4.5 (2.5)</td><td>4.7 (2.4)</td><td>3.1 (2.8)</td><td>&lt;0.001</td><td>0.79 (0.71, 0.88)</td><td>&lt;0.001</td></tr><tr><td colspan="8"><strong>Intensity distribution</strong></td></tr><tr><td>Intensity intercept</td><td>12.4 (0.7)</td><td>12.3 (0.6)</td><td>12.7 (0.7)</td><td>&lt;0.001</td><td>1.20 (1.08, 1.34)</td><td>&lt;0.001</td></tr><tr><td>Intensity gradient</td><td>-2.08 (0.22)</td><td>-2.07 (0.21)</td><td>-2.20 (0.25)</td><td>&lt;0.001</td><td>0.82 (0.74, 0.91)</td><td>&lt;0.001</td></tr><tr><td colspan="8"><strong>Timing<sup>c</sup></strong></td></tr><tr><td>Timing of PA (hours)</td><td>10 h 12 min (1 h 36 min)</td><td>10 h 12 min (1 h 36 min)</td><td>10 h 0 min (1 h 48 min)</td><td>0.01</td><td>0.96 (0.87, 1.06)</td><td>0.41</td></tr></tbody><tfoot><tr><td colspan="8">Data are mean (standard deviation). Abbreviations: HR: hazard ratio; CI: confidence interval; SB: sedentary behaviour; LIPA: light-intensity physical activity; MVPA: moderate-to-vigorous physical activity; PA: physical activity.<sup>c</sup>Estimated using Student's t-test.<sup>d</sup>Associations of PA and SB features with mortality risk (HR for one standard deviation increment) were examined in separate models adjusted for sociodemographic, behavioural, health-related factors.<sup>e</sup>Timing of five most active hours during the waking period.</td></tr></tfoot></table>

period duration, a two composite scores solution was also identified, although features related to overall activity level and timing of PA dimensions were not selected (Supplementary Figure S3).

## Internal validation

In a model including traditional risk factors (sociodemographic, behavioural, and health-related factors) and composite scores 1 and 2, a 1-SD increase in composite score 1 was associated with a 10% increase in mortality risk (HR [95% CI]: 1.10 [1.05, 1.15], p < 0.001), while no association was found for composite score 2 (HR [95% CI]: 0.99 [0.89, 1.10], p = 0.84) (Fig. 2 and Table 3). Compared to a model including traditional risk

factors (Model 1), a model additionally adjusted for composite score 1 (Model 2) had better goodness of fit in terms of R² and AIC (ΔAIC [95% CI]: -19.3 [-45.1, -5.6]), better classification accuracy in terms of sensitivity and specificity, and had a significantly higher C-index (ΔC-statistic [95% CI]: 0.007 [0.002, 0.014]) as shown in Table 3. When adding composite score 2 (Model 3) to this model (Model 2), a lower sensitivity but greater specificity was found, but the predictive performance was similar in terms of AIC and C-statistics (Table 3). In absence of association between composite score 2 and mortality and of performance gain from a model adding composite score 2 to one including composite score 1, further analyses were conducted using composite score 1 only. Similar results were
<figure><img src="image_3.png" /><figcaption>**Fig. 1:** Factor loadings of physical activity and sedentary behaviour variables in composite scores identified as predictors of mortality risk. PA: physical activity; SB: sedentary behaviour; MVPA: moderate-to-vigorous physical activity; LIPA: light-intensity physical activity. Dark grey: factor loading absolute value ≥0.2; light grey: factor loading absolute value <0.2.</figcaption></figure>

observed in sensitivity analysis using composite scores based on standardised PA and SB features on waking period duration both for comparison of Model 2 to Model 1 (ΔAIC [95% CI] and ΔC-statistic [95% CI]: -22.0 [-49.4, -7.6] and 0.007 [0.002, 0.015], respectively) and of Model 3 to Model 2 (ΔAIC [95% CI] and ΔC-statistic [95% CI]: 2.0 [-8.4, 2.0] and -0.00001 [-0.0003, 0.004], respectively) (Supplementary Table S2).

The association of composite score 1 with mortality and its added predictive value were examined in subgroups defined by age (<74 and ≥74 years), sex, BMI (normal, overweight, and obesity), and morbidity status (subgroup characteristics shown in Supplementary Table S3). A significant association between composite score 1 and mortality was observed in all subgroups (Fig. 3). A 1-SD increase in the composite score was

<figure><img src="image_4.png" /><figcaption>**Fig. 2:** Association of the physical activity and sedentary behaviour composite scores with mortality in Whitehall II accelerometer study. Estimated using a model including sociodemographic, behavioural, health-related factors, and both composite scores 1 and 2. Shaded black and blue areas around estimates based on linear term and restricted cubic splines represent 95% confidence intervals.</figcaption></figure>
<table><thead><tr><th></th><th>HR (95% CI)<br>PA and SB composite scores</th><th>Royston's R<sup>2</sup><br>(95% CI)</th><th>AIC<br>(95% CI)</th><th>ΔAIC<br>(95% CI)</th><th>ΔAIC<br>(95% CI)</th><th>Sensitivity %<br>(95% CI)<sup>a</sup></th><th>Specificity %<br>(95% CI)<sup>a</sup></th><th>C-index<br>(95% CI)</th><th>ΔC-index<br>(95% CI)</th><th>ΔC-index<br>(95% CI)</th></tr></thead><tbody><tr><td>Model 1 <sup>b</sup></td><td>NA</td><td>0.366<br>(0.328, 0.424)</td><td>6374.6<br>(5801.3, 6989.1)</td><td>Ref.</td><td>NA</td><td>69.9<br>(56.4, 87.9)</td><td>66.4<br>(47.1, 82.3)</td><td>0.751<br>(0.731, 0.778)</td><td>Ref.</td><td>NA</td></tr><tr><td>Model 2 <sup>c</sup></td><td>PA and SB score 1:<br>1.10 (1.06, 1.14), p &lt; 0.001</td><td>0.382<br>(0.344, 0.439)</td><td>6355.3<br>(5794.5, 6960.7)</td><td>-19.3<br>(-45.1, -5.6)<sup>b</sup></td><td>Ref.</td><td>70.3<br>(53.2, 85.2)</td><td>67.1<br>(51.4, 83.7)</td><td>0.758<br>(0.738, 0.784)</td><td>0.007<br>(0.002, 0.014)<sup>e</sup></td><td>Ref.</td></tr><tr><td>Model 3 <sup>d</sup></td><td>PA and SB score 1:<br>1.10 (1.05, 1.15), p &lt; 0.001<br>PA and SB score 2:<br>0.99 (0.89, 1.10), p = 0.84</td><td>0.381<br>(0.345, 0.439)</td><td>6357.2<br>(5798.4, 6963.5)</td><td>-17.4<br>(-44.5, -4.3)<sup>e</sup></td><td>2.0<br>(-7.5, 1.9)</td><td>67.2<br>(50.5, 86.1)</td><td>70.4<br>(52.9, 86.1)</td><td>0.758<br>(0.738, 0.784)</td><td>0.007<br>(0.003, 0.014)<sup>e</sup></td><td>-0.0001<br>(-0.0003, 0.0041)</td></tr></tbody></table><p>Abbreviations: CI: confidence interval; AIC: Akaike information criterion; C-index: Harrell's C-index. <sup>a</sup>Youden index cutoff points for the calculation of the sensitivity and specificity; 0.363 (95% CI: [0.301, 0.475]) for model 1, 0.374 (95% CI: [0.318, 0.489]) for model 2 and 0.381 (95% CI: [0.354, 0.424]) for Model 3. <sup>b</sup>Model 1: Model includes socio-demographic, behavioural, and health-related factors. <sup>c</sup>Model 2: Model 1 additionally adjusted for the physical activity and sedentary behaviour composite score 1. <sup>d</sup>Model 3: Model 2 additionally adjusted for the physical activity and sedentary behaviour composite score 2. <sup>e</sup>Confidence intervals (calculated based on 1000 bootstrap samples) not containing zero indicate significant differences in predictive performance between Model 1 and Model 2, and between Model 2 and Model 3.</p><p><b>Table 3:</b> Predictive performance of physical activity and sedentary behaviour composite scores 1 and 2 for mortality risk in the Whitehall II accelerometer sub-study (N cases/N total = 410/3991, mean [standard deviation] follow-up = 8.1 [1-3] years).</p><p>Table 3: Predictive performance of physical activity and sedentary behaviour composite scores 1 and 2 for mortality risk in the Whitehall II accelerometer sub-study (N cases/N total = 410/3991, mean [standard deviation] follow-up = 8.1 [1-3] years).</p><p>associated with a higher mortality risk ranging from 8% among those aged ≥74 years (HR [95% CI]: 1.08 [1.02, 1.14], p = 0.005) to 12% in participants aged &lt;74 years old (1.12 [1.06, 1.18], p &lt; 0.001), women (1.12 [1.02, 1.23], p = 0.013), and overweight participants (1.12 [1.05, 1.20], p &lt; 0.001). The association was marginally significant among participants with obesity (1.11 [1.00, 1.23], p = 0.041).</p><p>Compared to a model adjusted for traditional risk factors (Model 1), including composite score 1 improved the predictive accuracy in terms of C-index among all sub-groups although the improvement did not reach significance for all groups (Fig. 3). Significant improvement in predictive performance for mortality was observed among participants &lt;74 years (ΔC-index [95% CI]: 0.012 [0.003, 0.031]), men (0.006 [0.002, 0.015]), women (0.011 [0.001, 0.036]), participants with normal BMI (0.006 [0.000, 0.018]), overweight participants (0.008 [0.001, 0.022]), and participants with at least one chronic disease (0.011 [0.003, 0.026]). A similar trend was observed among those aged ≥74 and participants with obesity, although no significant difference in C-index was found for these groups. Results were consistent when considering the AIC of the models (Supplementary Tables S4-S7) instead of C-index, except for women (Supplementary Table S5) and participants with normal BMI (Supplementary Table S6) where the CI of the ΔAIC included zero.</p><h3>External validation</h3><p>A total of 1329 participants aged ≥60 years (mean age: 69.5 years, 56.7% women) of the CoLaus accelerometer sub-study composed the validation cohort (Supplementary Methods 3, Supplementary Figure S4, Supplementary Table S8), of whom 105 died after a mean (SD) follow-up of 3.8 (0.7) years. Compared to Whitehall II, participants of CoLaus were more likely to be women (25.8% in Whitehall II vs 56.7% in CoLaus), of white ethnicity (92.6 vs 95.0%), to live alone (25.3 vs 56.7%), smoke (5.5 vs 14.6%), have a lower alcohol intake (20.3 vs 24.5%) and higher consumption of fruit and vegetables (59.0 vs 69.2%). The proportion of participants with a high BMI (18.1 vs 22.1%) and hypertension (51.7 vs 55.1%) was higher in CoLaus, while hyperlipidemia (50.6 vs 38.0%), morbidity (40.8 vs 20.8%), and functional limitations (9.7 vs 8.5%) were more frequent in Whitehall II. In addition, participants included in CoLaus tended to have higher PA and SB levels (mean [SD] acceleration: 31.8 [9.7] mg in Whitehall vs 38.2 [11.8] mg in CoLaus), compared to participants from Whitehall II, which might partly reflect difference in wrist placement between the studies (dominant in CoLaus, non-dominant in Whitehall).</p><p>As in the Whitehall II, there was no evidence of a non-linear relationship of composite score 1 with mortality (p = 0.87). A 1-SD increase in composite score 1</p>

## External validation
<figure><img src="image_5.png" /><figcaption>**Fig. 3:** Association of the physical activity and sedentary behaviour composite score 1 with mortality risk and its predictive performance by subgroups defined by age, sex, body mass index, and morbidity status in Whitehall II accelerometer sub-study. C-index: Harrell's C-index. Model 1: Model includes socio-demographic, behavioural, and health-related factors. Model 2: Model 1 additionally adjusted for the physical activity and sedentary behaviour composite score 1. * Confidence intervals not containing zero indicate significant difference in predictive performance between Model 1 and Model 2.</figcaption></figure>

was associated with a 18% higher mortality in this validation cohort (HR [95% CI]: 1.18 [1.10, 1.26], p < 0.001) (Fig. 4). Compared to the model including traditional risk factors, the model additionally adjusted for composite score 1 showed a significantly better predictive performance (ΔAIC [95% CI]: -17.8 [-41.8, -4.3]; ΔC-statistic [95% CI]: 0.029 [0.002, 0.055]; Table 4).

To our knowledge, few studies have previously investigated the effectiveness and added value of the objectively-assessed features of PA and SB in predicting

Discussion

This study using accelerometer-derived measures of PA and SB features in older adults presents three key findings. One, overall activity level, bouts duration, frequency, and intensity distribution were identified as important dimensions of movement behaviours associated with risk of mortality in older adults, in addition to the currently-recommended total duration in MVPA. Two, the developed composite score comprising 11 modifiable PA and SB features predicted mortality risk above and beyond socio-demographic, behavioural, and health-related risk factors. Subgroup analysis suggested that the score was associated with mortality in subgroups defined by age, sex, BMI, and morbidity status; its added predictive value reached significance both in men and women, and in the younger group (<74 years), those presenting morbidity, and in groups without obesity. Three, the association of the PA and SB composite score with mortality and its added predictive value was confirmed in the external validation cohort.

<figure><img src="image_6.png" /><figcaption>**Fig. 4:** Association of the physical activity and sedentary behaviour composite score 1 with mortality risk in the CoLaus study. Estimated using a model including sociodemographic, behavioural, health-related factors, and composite score 1. Shaded black and blue areas around estimates based on linear term and restricted cubic splines represent 95% confidence intervals.</figcaption></figure>
<table><thead><tr><th></th><th>HR (95% CI)<br>PA and SB composite score 1</th><th>Royston's R<sup>2</sup><br>(95% CI)</th><th>AIC<br>(95% CI)</th><th>ΔAIC<br>(95% CI)</th><th>Sensitivity %<br>(95% CI)<sup>a</sup></th><th>Specificity %<br>(95% CI)<sup>b</sup></th><th>C-index<br>(95% CI)</th><th>ΔC-index<br>(95% CI)</th></tr></thead><tbody><tr><td>Model 1<sup>c</sup></td><td>NA</td><td>0.392<br>(0.349, 0.556)</td><td>1121.4<br>(876.0, 1308.8)</td><td>Ref.</td><td>56.1<br>(49.5, 90.3)</td><td>84.2<br>(50.8, 90.3)</td><td>0.757<br>(0.716, 0.830)</td><td>Ref.</td></tr><tr><td>Model 2<sup>b</sup></td><td>1.18<br>(1.10, 1.26), p &lt; 0.001</td><td>0.430<br>(0.394, 0.593)</td><td>1103.6<br>(856.7, 1287.9)</td><td>-17.8<br>(-41.8, -4.3)<sup>d</sup></td><td>74.2<br>(55.4, 88.8)</td><td>75.4<br>(56.4, 90.4)</td><td>0.785<br>(0.737, 0.856)</td><td>0.029<br>(0.002, 0.055)<sup>d</sup></td></tr></tbody></table>

CI: confidence interval; AIC: Akaike information criterion; C-index: Harrell's C-index; PA, physical activity; SB, sedentary behaviour. <sup>a</sup>Youden index cutoff points for the calculation of the sensitivity and specificity: 0.403 (95% CI: [0.345, 0.578]) for Model 1 and 0.496 (95% CI: [0.391, 0.628]) for Model 2. <sup>b</sup>Model 2: Model 1 additionally adjusted for the physical activity and sedentary behaviour composite score 1. <sup>c</sup>Model 1: Model includes socio-demographic, behavioural, and health-related factors. <sup>d</sup>Confidence intervals (calculated based on 1000 bootstrap samples) not containing zero indicate significant differences in predictive performance between Model 1 and Model 2.

<caption>**Table 4:** Predictive performance of the physical activity and sedentary behaviour composite score 1 for mortality risk in CoLaus accelerometer sub-study (N cases/N total = 105/1329, mean [standard deviation] follow-up = 3.8 [0.7] years).</caption>

mortality in older adults.<sup>13-16</sup> One prospective study using data from The Rush Memory and Aging Project (N = 1249; mean age: 80 years), focused on features measured during specific gait tests such as Timed Up and Go test.<sup>13</sup> Other studies examined daily life activities in participants aged ≥50 from the UK Biobank<sup>14</sup> or the National Health And Nutrition Examination Survey.<sup>15,16</sup> They included seven to 25 features covering up to 4 dimensions of movement behaviour and used variables selection methods that do not take into account the high correlation between these features. They identified several features to be among the strongest predictors of 5-year mortality in comparison with other traditional mortality predictors. Overall activity<sup>14,15</sup> and active to sedentary transition probability,<sup>15,16</sup> a marker of the fragmentation of activity bouts as mean duration of activity bouts in the present study, were selected in two different studies, but heterogeneity between studies in included features and in ranking methodologies makes further comparisons difficult. None of these studies examined the external validity of findings, as recommended for studies using a machine learning approach,<sup>28</sup> limiting the generalisability of the findings.

We extend previous studies by developing and validating a PA and SB composite score for mortality risk using a machine learning approach adapted to correlated data that simultaneously performs variable selection and dimension reduction. Dimensions investigated in previous studies were included as well as two additional dimensions (frequency and intensity distribution). The composite score derived from 11 features distributed in five dimensions of movement behaviour was found to predict mortality above and beyond traditional sociodemographic, behavioural, and health-related risk factors both in the derivation and validation cohorts, indicating generalisability of findings. In addition to the currently recommended guidelines of longer time in MVPA across several days of the week,<sup>4-6</sup> we identified higher frequency of LIPA and MVPA bouts, more fragmented SB, and a more uniform (higher gradient and lower intercept) intensity

distribution to be associated with reduced risk of mortality in older adults. The magnitude of factors loadings of these features was similar as the one of total duration in MVPA, denoting their equal contribution. Contrary to the recommendation of reducing sedentary time found in recent guidelines,<sup>4-6</sup> we did not find evidence that total time in SB was an important predictor of mortality, while the way it was accumulated appeared to be important.

The composite score developed in the present study was associated with mortality in all subgroups defined by sex, age, BMI, and morbidity status, although the degree to which it improved predictive accuracy compared to traditional risk factors varied between subgroups. The composite score did not improve mortality risk prediction in participants aged ≥74 years. We observed that this oldest old group had overall low levels of PA and high sedentarity with low variability, suggesting that limitations in the functional capacity itself may partially explain the reduced ability of the composite score to predict risk of mortality in line with a previous hypothesis.<sup>29</sup> The added predictive value of the composite score was also uncertain for participants with obesity although the lower sample size for this group may explain lack of significance. There was no clear evidence of added value of the composite score in predicting mortality in participants without chronic diseases. The progression to death in these individuals located upstream of the morbidity-mortality process may be more complex and heterogeneous, and may involve a larger set of predictive factors, diminishing the added importance of any one of them compared to the whole. In contrast, the added predictive value of the composite score found among participants with chronic diseases highlights the importance of physical activity in monitoring the progression of chronic diseases and its rehabilitation.<sup>30,31</sup>

Our study has limitations. First, although we used data on a large set of PA and SB features, accelerometry measurement does not provide information on posture and context of PA and SB, including use of assisting
device, precluding us from examining these two
important dimensions of movement behaviour. Second,
participants of the Whitehall II cohort were all employed
at recruitment in 1985–1988, predominantly men, with
better health than the general population. However, the
use of external (CoLaus independent cohort) validation
showed that these features have little influence on the
robustness of our findings. Third, some of the covariates
were self-reported (behavioural factors and limitations
in ADL/IADL), their association with mortality may
have been underestimated leading to potential residual
confounding in the association between the composite
score and mortality. Finally, the focus of this study was
on identifying features of PA and SB during waking
period important for health as current PA guidelines
highlight this research need.³² However, there is
emerging research including sleep as part of the 24-h
movement behaviours. The way to examine sleep fea-
tures along with PA and SB features during the waking
period is still debated,³³ particularly among older adults
who tend to have more sleep interruptions, although
independently from their individual choices. Identifica-
tion of 24-h PA, SB, and sleep features that predict
mortality requires further investigation.

Though there have been recent changes in PA
guidelines as the measurement of movement behav-
iours has improved,⁷ adherence to current recommen-
dations of increasing time in MVPA while reducing
sedentary time among older adults remains poor.⁹,¹⁰ It is
suggested that public health messages targeting total
duration of PA or SB, such as 150 min/week of MVPA,
are perceived as (1) unattainable, (2) far from the targets
people are willing to achieved, and (3) embedded within
negative representations linking PA with sport.¹¹,¹² The
present study identified 10 PA and SB features, in
addition to time in MVPA, associated with mortality risk
that could be used as new additional levers of action in
tailored interventions in a population that maintains low
levels of activity. The precise design of these in-
terventions is beyond the scope of this study and re-
quires further investigations but, overall, diversification
of physical activity targets, for example, through tools
promoting breaks in sitting activities or personalized
messages scheduled to incite people to go for a walk
might be an avenue for new interventions. Wearable
devices are increasingly used at the population level and
could be an opportunity to detect individuals who will
benefit most from these interventions.

In conclusion, this study identified a set of PA and
SB features predicting mortality risk that might be
relevant, new targets for prevention. These features
relate to dimensions of movement behaviour that go
beyond total duration in MVPA or SB. Whether in-
terventions to modify these features among older adults
are easier to implement and overcome existing
individual<sup>34,35</sup> or environmental<sup>36</sup> barriers warrants
further research.

Contributors

SS conceived the idea for the study. MC, MAB, VvH, BL, and SS designed the study methodology. MC, MAB, BL, and SS did the investigation. MC, MY, VvH, MAB, PMV, and SS accessed and validated the dataset. MC, MAB, and VvH did the formal data analysis. MC, VvH, PMV, and SS curated the data. MC, BL, and SS prepared the first draft of the manuscript. All authors reviewed and edited the manuscript. MC and MAB were responsible for the figures. MC is the guarantor. SS and MAB supervised the study. SS acquired the funding for the study.

Data sharing statement

Data, protocols, and other metadata of the Whitehall II study are avail-
able to the scientific community via the Whitehall II study data sharing
portal (https://www.ucl.ac.uk/epidemiology-health-care/research/
epidemiology-and-public-health/research/whitehall-ii/data-sharing). In-
formation related to CoLaus data access is available to qualified, inter-
ested researchers at https://www.colaus-psycolaus.ch/professionals/
how-to-collaborate/. All responses to data sharing requests must
comply with the ethical and legal constraints of Switzerland.

Declaration of interests

We declare no competing interests.

Acknowledgments

The authors thank all participating civil service departments and their
welfare, personnel, and establishment officers; the British Occupational
Health and Safety Agency; the British Council of Civil Service Unions;
all participating civil servants in the Whitehall II study; and all members
of the Whitehall II study team. The Whitehall II Study team comprised
research scientists, statisticians, study coordinators, nurses, data man-
agers, administrative assistants, and data entry staff who made the study
possible. These individuals and organizations were not compensated for
this work. The authors thank CoLaus study participants and team
members. The Whitehall II study was and is supported by the US Na-
tional Institutes of Health National Institute on Aging (grants
R01AG056477 and RF1AG062553), the UK Medical Research Council
(grants R024227, S011676, and K013351), the British Heart Foundation
(grant RG/16/11/32334), and the Wellcome Trust (grant 221854/Z/20/
Z). The CoLaus study was and is supported by research grants from
GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and
the Swiss National Science Foundation (grants 3200B0-105993, 3200B0-
118308, 33CSCO-122661, 33CS30-139468, and 33CS30-148401). Dr
Chen, Dr Yerramalla, and Dr Sabia were supported by the French Na-
tional Research Agency (grant ANR-19-CE36-0004-01). Mikaela Bloom-
berg is supported by the Economic and Social Research Council (ES/
P000592/1, https://esrc.ukri.org/).

**Appendix A. Supplementary data**

Supplementary data related to this article can be found at https://doi.
org/10.1016/j.eclinm.2022.101773.

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