Ambulatory Depression Dynamics Dataset

Columns

• phq1-phq9: Patient Health Questionnaire-9 (PHQ-9) score for the first through ninth days, respectively. These are numeric scores indicating the severity of depressive symptoms, typically ranging from 0 to 3 based on observed quantiles.
• age: The participant's age. This is a numeric field.
• sex: The participant's gender. This is a categorical field, with categories such as female, male, and other.
• q10, q11, q12, q13, q14, q16, q46, q47: These columns represent Patient Health Questionnaire-9 (PHQ-9) scores for specific days (tenth, eleventh, twelfth, thirteenth, fourteenth, sixteenth, forty-sixth, and forty-seventh day). These questions cover various mental health subjects including anhedonia, fatigue, sleep disturbance, and changes in appetite or weight, rated on a numeric scale from 0 to 4.
• happiness.score: This numeric score reflects an individual's subjective happiness rating at the time of assessment, with higher scores indicating greater positivity towards life as reported by the participant. The scale ranges from 0 to 4.
• time: The time of day the assessment was conducted. This is a categorical field, often represented by date ranges.
• period.name: The named period during which the assessment was taken (e.g., evening, midday). This is a categorical field.
• start.time: The start time of the period in which the assessment was taken. This is a numeric field, often represented by date ranges.
• phq.day: The total number of days that have passed since the beginning of the clinical trial at the start of the assessment period. This is a numeric field.

Distribution

Usage

• Developing AI-driven screening tools that can swiftly identify and monitor symptoms of depression. Such a tool could integrate PHQ-9 responses and other ambulatory assessment data to gather detailed insights into mood changes over time, offering more precise and tailored detections of depression.
• Investigating potential correlations between age, gender, PHQ scores, and daily happiness scores to better understand which individuals might be at increased risk for developing depression.
• Gaining a better understanding of how daily moods may be connected to longer-term mental health, providing valuable insight for medical professionals that could lead to improved treatment plans or interventions.
• Using the start time and period name variables as inputs for creating predictive models that seek out patterns in the occurrence of depressive symptoms over specific phases or times. This could help medical professionals identify when patients may need additional guidance or resources during certain periods in their lives.

Coverage

• Demographic Scope: It includes age data for participants, with 39 unique age values and approximately 40% of entries recorded as 'NA' for a specific count. Gender information is also provided, showing 67% female, 24% male, and 9% other, with 8% of gender data missing.
• Time Range: The assessments documented in the time column span from 30 September 2016 to 24 August 2017. The start.time column covers a period from 30 September 2016 to 12 January 2017.
• Assessment Length: While the primary focus is on 14 days of ambulatory assessment data, some PHQ-9 scores extend up to the forty-seventh day.

License

Who Can Use It

• Researchers: For detailed analysis of depression in general population samples, exploring relationships between variables over different durations.
• Medical Professionals: To enhance their understanding of daily mood dynamics, link them to long-term mental well-being, and inform treatment strategies or interventions.
• Data Scientists and AI Developers: To construct AI-powered tools for the screening and monitoring of depression.
• Public Health Analysts: To identify risk factors and contribute to better mental health outcomes for individuals affected by depression.

Dataset Name Suggestions

• Ambulatory Depression Dynamics Dataset
• PHQ-9 Mood Assessment Data
• General Population Depression Symptoms Study
• 14-Day Mood & Depression Insights
• Mental Health Ambulatory Assessment

Attributes