̽��ֱ�� of Cambridge - mortality

Fitness levels accurately predicted using wearable devices – no exercise required

sc604 — Thu, 01 Dec 2022 10:00:18 +0000

Normally, tests to accurately measure VO2max – a key measurement of overall fitness and an important predictor of heart disease and mortality risk – require expensive laboratory equipment and are mostly limited to elite athletes. ̽��ֱ��new method uses machine learning to predict VO2max – the capacity of the body to carry out aerobic work – during everyday activity, without the need for contextual information such as GPS measurements.

In what is by far the largest study of its kind, the researchers gathered activity data from more than 11,000 participants in the Fenland Study using wearable sensors, with a subset of participants tested again seven years later. ̽��ֱ��researchers used the data to develop a model to predict VO2max, which was then validated against a third group that carried out a standard lab-based exercise test. ̽��ֱ��model showed a high degree of accuracy compared to lab-based tests, and outperforms other approaches.

Some smartwatches and fitness monitors currently on the market claim to provide an estimate of VO2max, but since the algorithms powering these predictions aren’t published and are subject to change at any time, it’s unclear whether the predictions are accurate, or whether an exercise regime is having any effect on an individual’s VO2max over time.

̽��ֱ��Cambridge-developed model is robust, transparent and provides accurate predictions based on heart rate and accelerometer data only. Since the model can also detect fitness changes over time, it could also be useful in estimating fitness levels for entire populations and identifying the effects of lifestyle trends. ̽��ֱ��results are reported in the journal npj Digital Medicine.

A measurement of VO2max is considered the ‘gold standard’ of fitness tests. Professional athletes, for example, test their VO2max by measuring their oxygen consumption while they exercise to the point of exhaustion. There are other ways of measuring fitness in the laboratory, like heart rate response to exercise tests, but these require equipment like a treadmill or exercise bike. Additionally, strenuous exercise can be a risk to some individuals.

“VO2max isn’t the only measurement of fitness, but it’s an important one for endurance, and is a strong predictor of diabetes, heart disease, and other mortality risks,” said co-author Dr Soren Brage from Cambridge’s Medical Research Council (MRC) Epidemiology Unit. “However, since most VO2max tests are done on people who are reasonably fit, it’s hard to get measurements from those who are not as fit and might be at risk of cardiovascular disease.”

“We wanted to know whether it was possible to accurately predict VO2max using data from a wearable device, so that there would be no need for an exercise test,” said co-lead author Dr Dimitris Spathis from Cambridge’s Department of Computer Science and Technology. “Our central question was whether wearable devices can measure fitness in the wild. Most wearables provide metrics like heart rate, steps or sleeping time, which are proxies for health, but aren’t directly linked to health outcomes.”

̽��ֱ��study was a collaboration between the two departments: the team from the MRC Epidemiology Unit provided expertise in population health and cardiorespiratory fitness and data from the Fenland Study – a long-running public health study in the East of England – while the team from the Department of Computer Science and Technology provided expertise in machine learning and artificial intelligence for mobile and wearable data.

Participants in the study wore wearable devices continuously for six days. ̽��ֱ��sensors gathered 60 values per second, resulting in an enormous amount of data before processing. “We had to design an algorithm pipeline and appropriate models that could compress this huge amount of data and use it to make an accurate prediction,” said Spathis. “ ̽��ֱ��free-living nature of the data makes this prediction challenging because we’re trying to predict a high-level outcome (fitness) with noisy low-level data (wearable sensors).”

̽��ֱ��researchers used an AI model known as a deep neural network to process and extract meaningful information from the raw sensor data and make predictions of VO2max from it. Beyond predictions, the trained models can be used for the identification of sub-populations in particular need of intervention related to fitness.

̽��ֱ��baseline data from 11,059 participants in the Fenland Study was compared with follow-up data from seven years later, taken from a subset of 2,675 of the original participants. A third group of 181 participants from the UK Biobank Validation Study underwent lab-based VO2max testing to validate the accuracy of the algorithm. ̽��ֱ��machine learning model had strong agreement with the measured VO2max scores at both baseline (82% agreement) and follow-up testing (72% agreement).

“This study is a perfect demonstration of how we can leverage expertise across epidemiology, public health, machine learning and signal processing,” said co-lead author Dr Ignacio Perez-Pozuelo.

̽��ֱ��researchers say that their results demonstrate how wearables can accurately measure fitness, but transparency needs to be improved if measurements from commercially available wearables are to be trusted.

“It’s true in principle that many fitness monitors and smartwatches provide a measurement of VO2max, but it’s very difficult to assess the validity of those claims,” said Brage. “ ̽��ֱ��models aren’t usually published, and the algorithms can change on a regular basis, making it difficult for people to determine if their fitness has actually improved or if it’s just being estimated by a different algorithm.”

“Everything on your smartwatch related to health and fitness is an estimate,” said Spathis. “We’re transparent about our modelling and we did it at scale. We show that we can achieve better results with the combination of noisy data and traditional biomarkers. Also, all our algorithms and models are open-sourced and everyone can use them.”

“We’ve shown that you don’t need an expensive test in a lab to get a real measurement of fitness – the wearables we use every day can be just as powerful, if they have the right algorithm behind them,” said senior author Professor Cecilia Mascolo from the Department of Computer Science and Technology. “Cardio-fitness is such an important health marker, but until now we did not have the means to measure it at scale. These findings could have significant implications for population health policies, so we can move beyond weaker health proxies such as the Body Mass Index (BMI).”

̽��ֱ��research was supported in part by Jesus College, Cambridge and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Cecilia Mascolo is a Fellow of Jesus College, Cambridge.

Reference:
Dimitris Spathis et al. ‘Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments.’ npj Digital Medicine (2022). DOI: 10.1038/s41746-022-00719-1

Cambridge researchers have developed a method for measuring overall fitness accurately on wearable devices – and more robustly than current consumer smartwatches and fitness monitors – without the wearer needing to exercise.

You don’t need an expensive test in a lab to get a real measurement of fitness – the wearables we use every day can be just as powerful, if they have the right algorithm behind them

Cecilia Mascolo

Oscar Wong via Getty Images

Woman checking her smart watch and mobile phone after run

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Online atlas explores north-south divide in childbirth and child mortality during Victorian era

sc604 — Tue, 15 May 2018 07:36:56 +0000

̽��ֱ��Populations Past website is part of the Atlas of Victorian Fertility Decline research project based at the ̽��ֱ�� of Cambridge, in collaboration with the ̽��ֱ�� of Essex. It displays various demographic and socio-economic measures calculated from census data gathered between 1851 and 1911, a period which saw immense social and economic change as the population of the UK more than doubled, from just under 18 million to over 36 million, and industrialisation and urbanisation both increased rapidly.

̽��ֱ��atlas allows users to select and view maps of a variety of measures including age structure, migration status, marriage, fertility, child mortality and household composition. Users can zoom in to an area on the map and compare side-by-side maps showing different years or measures.

̽��ֱ��maps reveal often stark regional divides. “Geography plays a major role in pretty much every indicator we looked at,” said Dr Alice Reid from Cambridge’s Department of Geography, who led the project. “In 1851, more than one in five children born in parts of Greater Manchester did not survive to their first birthday. In parts of Surrey and Sussex however, the infant mortality rate at the same time was less than a third that number.”

While there are broad north-south divides in most of the maps, patterns at a local level were more complicated: in the northern urban-industrial centres such as Manchester, infant and child mortality were high, while many rural areas of the north had mortality rates as low as rural areas of the south. And in London, there is a sharp east/west divide in fertility, infant mortality, the number of live-in servants, and many other variables.

̽��ֱ��researchers also found that different types of industry were often associated with different types of families: in coal mining areas where there was little available work for women, women married young and often ended up with large families. In contrast, women in the textile-producing areas of Lancashire and Yorkshire had more opportunities to earn a wage, and perhaps consequently, had fewer children on average.

There are also big differences over time. ̽��ֱ��period saw a sharp drop in the number of women who continued to work after marriage, for instance. In 1851, more than a third of married women were in work across large sections of the country, but by 1911, only a tiny fraction of married women worked outside the home, apart from the textile-producing areas of the Northwest.

“This might be associated with the rise of the culture of female domesticity: the idea that a woman’s place is in the home,” said Reid.

Across the Western world, fertility rates have declined over the past 150 years. Gaining a historical perspective of how and why these trends have developed can help improve understanding of the way in which modern societies are shaped.

Between 1851 and 1911, England and Wales changed from countries where there were variable fertility and mortality rates to countries where rates for both were low. Child mortality and fertility fell from the 1870s, together with a fall in illegitimacy, but infant mortality did not start to fall until the dawn of the twentieth century.

As part of the project on fertility decline, the researchers have investigated fertility in more detail. For the first time, they have been able to calculate age-specific fertility rates for more than 2000 sub-districts across England and Wales during this era, and their results challenge views on the way that fertility fell.

“It’s long been thought that the fall in fertility was achieved when couples decided how many children they wanted at the outset of their marriage, and stopped reproducing once they had reached that number,” said Reid. “While this may have happened in more recent fertility transitions, such as in South-East Asia and Latin America, when reliable contraception was widely available, it was not a realistic scenario in the Victorian era.”

“We don’t find age patterns of fertility which would be produced by this type of ‘stopping’ behaviour during the Victorian fertility decline,” said Reid’s collaborator Dr Eilidh Garrett from the ̽��ֱ�� of Essex. “Such behaviour would show up as a larger reduction of fertility among older women, but instead, women of all ages appear to have been reducing their fertility.”

As well as the interactive maps, the Populations Past site provides a variety of resources for researchers, teachers and students at all levels. ̽��ֱ��research was funded by the Economic & Social Research Council and the Isaac Newton Trust.

A new interactive online atlas, which illustrates when, where and possibly how fertility rates began to fall in England and Wales during the Victorian era has been made freely available from today.

In 1851, more than one in five children born in parts of Greater Manchester did not survive to their first birthday. In parts of Surrey and Sussex however, the infant mortality rate at the same time was less than a third that number.

Alice Reid

Populations Past

Early childhood mortality rates in 1851 (left) and 1911 (right). ̽��ֱ��highest rates are in red and the lowest in blue.

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

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̽��ֱ��importance of grandmothers in the lives of their grandchildren

bjb42 — Thu, 29 Oct 2009 00:00:00 +0000

According to the "Grandmother Hypothesis," post-menopausal women can increase their genetic contribution to future generations by increasing the survivorship of their grandchildren.

While some demographic studies have found evidence for this theory, others have found little support for it.

A team led by biological anthropologist Leslie Knapp in the Department of Biological Anthropology have discovered that a grandmother's effect on grandchildren varies according to their X-chromosome relatedness.

̽��ֱ��research was carried out by a re-evaluation of the birth and death records of seven populations in Asia, North America, Europe and Africa who had lived in different periods dating back to the 17th century.

By specifically looking at child mortality in the first three years of life it was found that a grandmothers' effect on grandchildren varies according to their X-chromosome relatedness.

It was discovered that the effect of a grandmother's presence on grandchild survivorship corresponds relatively with her X-relatedness to the grandchild, which is not equivalent in boys and girls.

Specifically, maternal grandmothers have 25% X relatedness with both grandsons and granddaughters and both grandchildren are equally likely to inherit any one of her X-linked genes.

Contrastingly, paternal grandmothers will pass on one of her X chromosomes to their granddaughters (making them 50% X-related) but she will not pass this chromosome on to her grandson (making them 0% X-related).

Molly Fox, Gates Cambridge Scholar at the Department of Biological Anthropology said : "We suggest that maternal and paternal grandmothers have different incentive to invest in grandsons and granddaughters, due to differences in genetic relatedness.

" ̽��ֱ��presence of a paternal grandmother in all seven of the populations had a harmful effect on grandsons because her presence was linked with an increase in mortality.

"Meanwhile, in six out of seven populations, the paternal grandmother's presence in her granddaughter's early life had a beneficial effect in terms of the risk of mortality. This difference between paternal grandsons and granddaughters would explain a lot of the inconsistencies in previous studies, where the sex of the grandchild was not considered.

"We've only looked at child mortality, and the mechanism itself remains mysterious. Other studies have given evidence against conscious favouritism towards one grandchild or another".

It is widely believed that women live long post-reproductive lives to help care for their grandchildren and the "Grandmother Hypothesis" is based on the fact that women are genetically related to their grandchildren. ̽��ֱ��results suggest that the nature of that genetic relatedness should not be overlooked since boys and girls differ in the percent of genes they share with maternal versus paternal grandmothers based on differences in X-chromosome inheritance.

Biologists use genetic relatedness between family members to explain the evolution of not only longevity, but also altruism, kin investment, offspring recognition, parenting strategies, and tribe formation, and so reconsidering the genetic relatedness between grandmothers and grandchildren has implications throughout the field of human evolution.

It is widely believed that women live long post-reproductive lives to help care for their grandchildren. Now research suggests that the pattern may differ depending on the relationship between grandmother and grandchild.

We suggest that maternal and paternal grandmothers have different incentives to invest in grandsons and granddaughters, due to differences in genetic relatedness.

Molly Fox

dok1 from Flickr

Korean Grandmother and Baby

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