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Millions of people are affected by diabetes. For many people, medication plays a vital role in managing diabetes, but lifestyle can also be important in how both diabetes and prediabetes are managed. For example, previous research has shown that being more active and getting better quality sleep can be important in diabetes and prediabetes management.

Wearable devices offer a reliable way to capture people’s day-to-day routines, avoiding the need for people to recall their activities.

The study in this month’s Tap into Research looked at how smartwatches were used to track steps and sleep in 796 participants from the larger Framingham Heart Study.

How were smartwatches used in the study?

Participants who owned an iPhone were given an Apple Watch to record their steps and heart rate. This data was collected from participants for up to three years. To be included in the study, participants needed to wear their smartwatch for at least 10 hours per day for 30 days or more.

Participants were asked to wear the watch while awake and take it off before bed. Sleep times were estimated based on when participants wore the watch. Putting it on in the morning was taken as their wake-up time, and taking it off at night marked their bedtime. Daily step counts were used as the measure of physical activity.

What did the study find?

The study included people with diabetes, people with prediabetes, and people without diabetes. All participants with diabetes were grouped together, whether they had type 1 or type 2 diabetes.

On average, people with diabetes took around 1611 fewer steps per day than those without diabetes. This was after accounting for age, sex, and ethnicity. Similarly, people with prediabetes took about 392 fewer steps per day than those without diabetes.

When it came to sleep, people with diabetes had more day-to-day variation in the time they first wore their watch (woke up) and last took it off (went to sleep). On average, their wake-up times varied by about 12 minutes more from day to day than those without diabetes, and their bedtimes varied by about 6 minutes more. People with prediabetes showed slightly more variation in their wake-up times (around 3 minutes) compared to people without diabetes, but not in their bedtimes.

Why does this matter?

Understanding daily routines in people living with diabetes could help identify lifestyle factors that support better self-management. This could include advice on how to achieve a more consistent sleep schedule or how to increase physical activity levels.

It’s worth noting that these findings come from one study, so more research is needed to confirm them. Still, the results highlight how wearable devices can offer new insights into how everyday routines, like sleep and how we move, relate to health.

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In Tap into Research, we often break down articles using data from smartwatches for health research. As well as in research, data from these devices can also play a role in individual care when shared with health providers, because this data reflects people’s day-to-day lives. However, this depends on two things: people wearing their devices and people being willing to share their data. There are many reasons why people might not want to do this. Understanding those reasons could help develop approaches that work better for participants and improve the quality of the data.

In this edition of Tap into Research, we are looking at a study that ran an online survey with 273 people aged 18 to 65, who either currently use or have previously used a smartwatch. They were asked about their experiences of using smartwatches and their views on sharing data.

What the survey found

The first questions explored reasons why people used their smartwatches regularly. These were:

1. Habit: People who built the device into their daily routine were more likely to keep wearing it.
2. User Satisfaction: A positive experience with the design, performance, and features made a difference.
3. Enjoyment: Using the device needed to feel enjoyable and rewarding.
4. Usefulness: While important, this ranked lower down the list.

The survey also asked about attitudes to data sharing. People said they were more comfortable sharing their health data through offline methods, like Bluetooth and NFC, rather than over the internet. They also preferred data to be fully anonymised, rather than only partially anonymised.

When it came to sharing data with their doctors, 66% of people felt confident that it could improve their care. But not everyone agreed. About 14% said they wouldn’t feel confident, and 20% were undecided.

Finally, people felt more comfortable sharing their data with doctors and health organisations than with private companies.

Why does this matter?

Because this study was voluntary and people signed up online, it may have attracted more tech-savvy or health-conscious individuals who use smartwatches. However, it still gave some useful insights into what makes people want to wear their devices, and the barriers to feeling comfortable with sharing data.  

When encouraging people to wear their devices, features that support habit-building and make the experience more enjoyable seem important. When it comes to data sharing, people have concerns about data privacy. Offering secure offline options to share data could make people more comfortable, and fully anonymising data where possible is preferred. But since full anonymisation is difficult to achieve, transparency and good communication with participants are key to building trust.

Determinants of continuous smartwatch use and data-sharing preferences with physicians, public health authorities, and Private companies: cross-sectional survey of smartwatch users

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For people living with depression, symptoms often change over time. At a doctor’s appointment, it can be hard to remember how you felt months ago, and because these symptoms go up and down, a single appointment may not show the full picture. But an accurate diagnosis is key to getting the best treatment.

One way to collect clearer symptom information could be via smartphones, especially as many people carry their phones with them everywhere. Could details like time spent on certain apps or the number of phone calls help measure depression severity?

In this edition of Tap into Research, we look at a study investigating what data from smartphones might reveal about how severe depression symptoms are.

How were smartphones used in the study?

For two weeks, participants shared data in two ways:

1. Actively collected data: People filled in daily surveys on their phones. This included rating their stress levels three times a day, sleep quality in the morning, and providing information on social interactions and nutrition at night.
2. Passively collected data: Phones collected information without participants having to input any information themselves, such as:

• Number of times phone locked and unlocked
• Number and length of calls
• Daily time spent on certain apps
• GPS location, number of places visited, and distance travelled.

What did the study find?

This study uses a statistic called “R-squared” to show how useful certain information is for estimating depression severity. It goes from 0% (not useful) to 100% (perfect). For example, knowing someone’s height might tell you a bit about their weight (maybe 60%), but not all of it. The other 40% could be explained by things like age, sex, and diet. This study used R-squared to see how well survey answers and smartphone data predicted depression severity.

People with more severe depression reported feeling more negative and having a lower quality of social interactions. Daily surveys explained about 35% of how severe depression was.

Using the information collected from people’s smartphones passively, patterns like longer calls and more screen time were associated with higher depression levels. This passive data explained about 20% of depression severity.

Using all the information collected from the daily surveys and passively from people’s smartphones explained about 45% of how severe depression was.

Why does this matter?

This study shows that smartphone data could help assess depression more accurately, but more research is needed with different groups of people, particularly in healthcare settings, to build confidence in these findings. It also raises important questions about how comfortable people feel sharing this smartphone data, showing that working with patients and the public is essential for moving forward in this research.

Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study

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Menopause is a natural part of ageing for women, when hormone levels change and periods stop. But for many women, it brings a wave of symptoms – hot flushes, night sweats, irritability, anxiety, and low self-esteem. Between 50-88% of women experience these symptoms, and some may last up to ten years after menopause.

We already know air pollution is bad for health. It can shorten life expectancy, raise the risk of heart disease, and even bring on menopause earlier. But could it also be making menopause symptoms worse?

Physical activity can help reduce menopause symptoms, but it’s unclear if the benefits are the same for women living in areas with different levels of air pollution. Other factors like body composition and cardiorespiratory health may also play a role.

In this edition of Tap into Research, we look at a study exploring the relationship between physical activity and menopause symptoms, and whether air pollution, obesity, and cardiorespiratory health make a difference. It involved 243 women from areas with both low and high air pollution.

How were smartphones and wearables used in the study?

Over two weeks, participants wore a Fitbit to track their daily step count. At the same time, they used a smartphone app to record their menopause symptoms.

Every evening, between 8 pm and 10 pm, participants used the app to rate how much 11 symptoms had bothered them that day. These symptoms included:   

• Psychological symptoms like low mood and irritability
• Physical symptoms like hot flushes, sleep problems, and joint pain
• Urogenital symptoms like bladder problems and vaginal dryness.

What did the study find?

Women living in areas with more air pollution reported more physical symptoms, like hot flushes and trouble sleeping, suggesting that living in highly polluted areas may make some menopause symptoms worse.

Interestingly, when women in these more highly polluted areas did more physical activity than usual, they reported fewer hot flushes.

This study found no link between obesity or fitness and menopause symptoms, which means other things like stress, hormones, or the environment might be more important. However, other studies have found links between higher body mass index (BMI) and menopause symptoms.

Overall, more physical activity was linked to less severe hot flushes, especially for women living in places with high air pollution.

Why does this matter?

These findings could help give better advice to women going through menopause. Knowing that being more active might help reduce hot flushes means physical activity could be a useful way to help manage symptoms.

The study also showed that menopause symptoms could be worse for women living in areas with more air pollution. Understanding this link better in the future could help researchers and healthcare providers find new ways to support women during menopause.

Physical activity and menopausal symptoms: evaluating the contribution of obesity, fitness, and ambient air pollution status

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Inflammatory bowel diseases (IBD), like Crohn’s disease and ulcerative colitis, can be difficult to manage. One of the challenges is dealing with flares – times when symptoms can return or get worse, even after a period of feeling well. These flares can be unpredictable and have a big impact on daily life.

A new study, including 309 people with IBD from across the United States, looked at whether data measured by smartwatches and smart rings could help identify and predict IBD flares.

How were smartphones and/or wearables used in the study?

Participants used their own Apple Watch, Fitbit, or Oura Ring, or could borrow one if needed. In total, 102 devices were loaned out to participants.

The following measurements were used from these devices:

• Heart rate (HR)
• Resting heart rate (RHR)
• Heart rate variability (HRV) – a measure of the variation in time between each heartbeat
• Step count

What did the study find?

Inflammatory flares (based on blood tests)

• HRV patterns changed during inflammatory flares, compared with remission.
• HR and RHR were higher during inflammatory flares.
• People took fewer steps during inflammatory flares.

Symptomatic flares (based on patient-reported symptoms)

• HR and RHR were also higher during symptomatic flares.
• There was no evidence that step count changed during symptomatic flares.

Predicting future flares

• Changes in HR, RHR, HRV, and step count began up to 7 weeks before a flare.
• Models including these measurements could predict whether a flare would happen in the future.

Why does this matter?

This study shows the value of monitoring people with IBD day-to-day – something that isn’t possible with healthcare appointments that are often weeks or months apart. The smartwatches and smart rings used in this study picked up changes in HR and HRV during flares, and even before symptoms appeared. These findings open the door to future research on how treatment could be adapted to improve outcomes for people living with IBD.

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While lockdowns and face masks may feel like a thing of the past, for many people, COVID-19 is still causing problems. Long COVID is when people still have symptoms like fatigue, brain fog, or breathlessness weeks or even months after getting sick.

But the symptoms of long COVID are not very well understood, and it’s not clear who is more likely to get long COVID.

A study published in September 2024 in The Lancet Digital Health aimed to find out more, using data from smartphones and wearable devices like Fitbits.

How were smartphones and/or wearables used in the study?

• People filled in surveys on a smartphone app. They shared information on their mental well-being and what COVID-19 symptoms they had.
• People also shared data from their Fitbits, such as their heart rate, how much they slept, and their step count.

What did the study find?

• Resting heart rates stayed higher than normal for up to four months after having COVID-19.
• Some people reported feeling more depressed and anxious for up to three months after getting COVID-19.
• Fatigue was the longest-lasting symptom after getting COVID-19. Some people still felt fatigued for more than 140 days.
• People who were more active before they got COVID-19 were less likely to get long COVID.

This study shows that smartphones and wearables could help track long-term illnesses, like long COVID. This could help researchers better understand the symptoms of these illnesses and allow doctors to use this information to treat patients better.

Physiological presentation and risk factors of long COVID in the UK using smartphones and wearable devices: a longitudinal, citizen science, case–control study

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A study published in July 2024 explored how the quality and duration of sleep affected long-term health risks. Using data from Fitbit smartwatches, researchers tracked each participant’s sleep over an average of 4.5 years.

They found that around seven hours of sleep per night was linked to the lowest risk of health issues. Getting more or less sleep raised the risk of conditions like high blood pressure and generalised anxiety disorder.

The study also looked at different sleep stages; people who had more REM (Rapid Eye Movement) sleep had a lower risk of heart rhythm issues, including atrial fibrillation. The Fitbit data gave a detailed look at sleep cycles that would be difficult to measure otherwise.

However, the study mostly included college-educated people, which means the findings may not apply to everyone equally. The researchers plan to address this in future work.

This kind of research shows how smartwatches can help us better understand the connection between sleep and health, potentially improving recommendations on healthy sleeping habits.

• Read the full paper