Assessing seasonal and weather effects on depression and physical activity using mobile health data

Methods:

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This study leveraged data from the RADAR-MDD researchprogram, which explored the effectiveness of remote mobile technologies for monitoring depression and predicting relapse in MDD49.The RADAR-MDD study recruited 623 participants from three study sites in the United Kingdom, Spain, and the Netherlands and followed them for up to 2 years50. Recruitment spanned November 2017 to June 2020, with data collection concluding in April 202150. Due to rolling enrollment, the follow-up duration varied from 11 months to 24months50.Utilizing the RADAR-base open-source platform, the RADAR-MDD program concurrently gathered both active (e.g., questionnaires) and passive (e.g., smartphone and Fitbit device) data51

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This study used data from a research program that tested whether phone and wearable technology could help monitor depression and predict when people might relapse. They recruited 623 people with depression across 3 countries (UK, Spain, Netherlands) for up to 2 years between 2017 and 2020. The follow-up duration with participants varied from 11 months to 2 years. During the study participants both actively answered questionaries on their phones and passively had data collected through their smartphones and Fitbit devices without having to do anything extra.

Introduction:

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Depression, recognized as themost prevalent mental disorder worldwide, is a leading cause of disability1. Despite its substantial economic and social burden2, the underlying etiology, pathophysiology, and effective treatments remain partially understood3–5. Previous research has shown that the variability of environmental factors (e.g., seasons andweather conditions)6–9 and individual behaviors (e.g., physical activity, social interaction, and sleep)10–14 can impact depression symptom severity and its long-term trajectory. However, the impact of such external environmental factors on depression may manifest differentially across individuals15–18.

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Depression is recognized as the most common mental disorder in the world and a leading cause of disability. Even though depression can be expensive and damaging to society, scientists still do not fully understand what causes it or how to treat it effectively. Previous research has shown seasonal conditions, and our personal behaviours such as physical activity and sleep can impact depressional symptoms and long-term effects. However, the impact of outside environmental factors on depression can be very different for each person.

Results:

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We analyzed 12,490 PHQ-8 questionnaires with corresponding weather information and Fitbit step count recordings from 428 participants per the data inclusion criteria (see “Methods”). The selected cohort had a median age of 50.0 years (IQR: 32.0–60.0) and was predominantly female (N = 331, 77.3%), with a median PHQ-8 score of 9.5 (IQR: 6.0–13.7). The median of individual average daily steps for study participants was 7135.5 (IQR: 4967.9–9267.3). Participants’ socio-demographics (except for gender) and PHQ-8 score distributionswere significantly different across the three study sites. Participants from CIBER (Spain) were the oldest and had the highest PHQ-8 scores. Further detailed socio-demographic information is provided in Table 1. Additionally, a sensitivity analysis showed no significant sociodemographic differences between the selected cohort in this study and the full RADAR-MDD study cohort.

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They analysed about 12,490 depression questionaries along with weather and step count data from 428 participants. Most participants were women around 50 years old. The Spanish participants were the oldest and had the highest depression scores. There were some demographic differences between the three countries but no major issues with the data quality.

Discussion:

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Our analysis of long-term data collected from a clinically depressed population in real-world settings demonstrated complex interplays between weather conditions, physical activity, and depression severity. Specifically, changes in weather conditions, such as temperature and day length, significantly influenced depression severity, which in turn impacted participants’ physical activity levels. Notably, these indirect influences manifested differently or even oppositely across participants with varying responses to weather. Additionally, our findings showed distinct patterns of depression severity variations across the seasons in the 1-year observational period in the present cohort.

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The long-term data showed that weather conditions, physical activity and depression are all connected. Specifically changed in temperature and how many hours of daylight there were had a big effect on how depressed people felt, which then affected how much they moved around. Interestingly this effect was different for many people, like for example for some people warmer weather made them feel better, for others it made them feel worse. The study also found that depression severity followed different seasonal patterns throughout the year depending on the person.

Your external environment is dependent on so many factors, but one factor I would like this study to explore in future research is on how an individual’s mental health is influenced by the people they surround themselves with. There’s a quote that goes “Show me your friends and I’ll show you your future” (John Wooden) and a lot of times your family, friends, or even your co-workers values, pressures, and habits can influence your own values, habits and potentially your mental health.

I felt like some of the tables and cluster analysis were a bit confusing. My mom is a researcher, so she was able to explain to me what it meant. All the 4 clusters were based on when the participants depression was the worst during the year.

Cluster 1 – stable depression year round

Cluster 2 – worst in spring

Cluster 3 – worst in winter

Cluster 4 – worst in autumn

The graphs and tables show these patterns were quite more complex with lots of statistical numbers.