{
  "format": "dataset-manifest/1",
  "slug": "virtual-reality-therapy-app",
  "version": 3,
  "seed": "virtual-reality-therapy-app:1",
  "engine": 1,
  "engineVersion": "1.0.0",
  "spec": {
    "format": "dataset/1",
    "slug": "virtual-reality-therapy-app",
    "name": "Virtual Reality Therapy Insights",
    "tagline": "Patient sessions, device usage, feedback, and emergency notifications from a virtual reality therapy application.",
    "description": "Patients interact with a virtual reality therapy application that offers innovative treatment options for psychological conditions. Each patient engages in immersive sessions designed to address their specific needs, while data regarding these interactions is meticulously recorded. Therapists track session frequency, device utilization, and patient feedback to evaluate the efficacy of the treatments. Most sessions proceed smoothly, but a minority activate emergency notifications due to adverse reactions, underscoring the critical nature of continuous monitoring in therapeutic settings. \n\nThe dataset comprises several interconnected tables: the 'patients' table details individual patient profiles, including demographics and psychological conditions; 'sessions' records each therapy session, including timestamps, duration, and associated patient identifiers; 'devices' catalogs the range of virtual reality equipment utilized during therapy, providing insights into technology efficiency; 'emergency_notifications' flags instances where patients required immediate attention due to negative experiences; and 'feedback_comments' captures qualitative feedback from patients about their sessions. A single row in each table corresponds to a specific entity: a patient, a therapy session, a device used, an emergency event, and a feedback entry respectively, allowing for rich analysis across the dataset. \n\nThis data answers essential questions about patient engagement, session effectiveness, and the impact of technology on therapy. It is particularly valuable for practitioners aiming to optimize treatment strategies and for researchers exploring the intersection of healthcare and technology. Additionally, the dataset is synthetic, designed to mimic realistic scenarios while allowing for practical application and testing of analytical skills.",
    "domain": "healthcare",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "virtual reality",
      "therapy",
      "healthcare",
      "patients",
      "sessions",
      "feedback",
      "notifications"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "patients": {
        "description": "Each row represents a unique patient participating in the therapy program, capturing their demographics and unique identifier.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each patient."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The patient's full name."
          },
          "age": {
            "kind": "int",
            "min": 18,
            "max": 75,
            "description": "Age of the patient.",
            "distribution": "normal"
          },
          "gender": {
            "kind": "enum",
            "values": [
              "male",
              "female",
              "other"
            ],
            "description": "Gender of the patient.",
            "weights": [
              6,
              3,
              1
            ]
          },
          "contact_email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address for the patient.",
            "nullable": true
          }
        }
      },
      "sessions": {
        "description": "Logs each therapy session attended by patients, capturing session details and the patient involved.",
        "rows": 800,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each session."
          },
          "patient_id": {
            "kind": "fk",
            "ref": "patients.id",
            "description": "References the patient attending the session."
          },
          "session_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the session took place.",
            "dayProfile": "waking"
          },
          "duration_minutes": {
            "kind": "int",
            "min": 30,
            "max": 120,
            "description": "Duration of the session in minutes.",
            "distribution": "normal"
          },
          "feedback_score": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Patient feedback score for the session.",
            "nullable": true,
            "distribution": "high"
          }
        }
      },
      "devices": {
        "description": "Details about the devices used during therapy sessions, linked to the sessions where they were utilized.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each device."
          },
          "session_id": {
            "kind": "fk",
            "ref": "sessions.id",
            "description": "References the session where the device was used."
          },
          "device_type": {
            "kind": "enum",
            "values": [
              "Oculus",
              "HTC Vive",
              "PlayStation VR",
              "Other"
            ],
            "description": "Type of VR device used.",
            "weights": [
              5,
              4,
              2,
              1
            ]
          },
          "usage_time": {
            "kind": "int",
            "min": 1,
            "max": 120,
            "description": "Time in minutes the device was used during the session.",
            "nullable": true,
            "when": {
              "column": "device_type",
              "in": [
                "Oculus",
                "HTC Vive",
                "PlayStation VR"
              ]
            },
            "distribution": "long-tail"
          },
          "battery_percentage": {
            "kind": "int",
            "min": 20,
            "max": 100,
            "distribution": "normal",
            "description": "Percentage of battery remaining on the device."
          }
        }
      },
      "emergency_notifications": {
        "description": "Records instances when emergency contacts were notified due to adverse reactions during sessions.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each emergency notification."
          },
          "session_id": {
            "kind": "fk",
            "ref": "sessions.id",
            "description": "References the session during which the emergency occurred."
          },
          "notification_time": {
            "kind": "datetime",
            "after": "session_id.session_date",
            "withinDays": 1,
            "description": "Timestamp when the emergency notification was sent."
          },
          "contact_name": {
            "kind": "fullName",
            "description": "Name of the emergency contact notified.",
            "nullable": true
          },
          "severity_level": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "distribution": "high",
            "description": "Severity of the incident leading to notification."
          }
        }
      },
      "feedback_comments": {
        "description": "Captures open-ended feedback from patients regarding their therapy sessions.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each feedback comment."
          },
          "session_id": {
            "kind": "fk",
            "ref": "sessions.id",
            "description": "References the session related to the feedback."
          },
          "comment": {
            "kind": "paragraph",
            "min": 10,
            "max": 500,
            "description": "Patient's written feedback about their experience.",
            "nullable": true
          },
          "response_time_days": {
            "kind": "int",
            "min": 0,
            "max": 30,
            "distribution": "normal",
            "description": "Days taken to respond to the feedback comment."
          },
          "is_anonymous": {
            "kind": "bool",
            "trueRate": 0.3,
            "description": "Indicates if the feedback was submitted anonymously."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the average feedback score across all therapy sessions?",
      "How often do emergency notifications occur during sessions?",
      "Which device type has the highest usage time and feedback score?",
      "Are there any trends in patient demographics affecting session outcomes?",
      "What common themes emerge from patient feedback comments?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Patients and Sessions",
        "sql": "SELECT p.full_name, s.session_date FROM patients p JOIN sessions s ON p.id = s.patient_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Feedback Score by Device Type",
        "sql": "SELECT d.device_type, AVG(s.feedback_score) FROM devices d JOIN sessions s ON d.session_id = s.id GROUP BY d.device_type;",
        "dialect": "postgres"
      },
      {
        "title": "Count Emergency Notifications by Month",
        "sql": "SELECT DATE_TRUNC('month', en.notification_time) AS month, COUNT(*) FROM emergency_notifications en GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      },
      {
        "title": "Patient Feedback Themes",
        "sql": "SELECT s.session_id, fc.comment FROM feedback_comments fc JOIN sessions s ON fc.session_id = s.id WHERE LENGTH(fc.comment) > 50;",
        "dialect": "postgres"
      },
      {
        "title": "Session Duration vs. Feedback Score",
        "sql": "SELECT s.duration_minutes, AVG(s.feedback_score) FROM sessions s GROUP BY s.duration_minutes ORDER BY s.duration_minutes;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Healthcare Analytics Team",
        "body": "The healthcare analytics team could utilize this dataset to evaluate the effectiveness of different virtual reality therapies. By analyzing session durations and feedback scores, they can identify which treatments yield better patient satisfaction, thereby guiding future therapeutic strategies."
      },
      {
        "title": "Data Engineers",
        "body": "Data engineers can employ this dataset for developing ETL pipelines that aggregate patient interaction data. By automating the extraction and transformation of this information, they can streamline reporting processes and enhance data accessibility for stakeholders."
      },
      {
        "title": "Clinical Researchers",
        "body": "Clinical researchers may explore this dataset to investigate the relationship between device usage and patient outcomes. By correlating device types with feedback and emergency notifications, they can provide insights that could improve device selection for therapy sessions."
      },
      {
        "title": "Healthcare IT Developers",
        "body": "Healthcare IT developers can leverage this dataset to build demo applications that simulate patient interactions within a virtual reality therapy environment. This allows stakeholders to visualize therapy scenarios and understand patient experiences in a controlled setting."
      }
    ]
  },
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    "sessions": 800,
    "devices": 500,
    "emergency_notifications": 50,
    "feedback_comments": 300
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