Solve the following sql problem:
Calculate the average age of people who have apps installed but are not active on their devices.

Schema:
SCHEMA:
- Table: app_all
  - Column: app_id
    - Samples: [-9223281467940916832, -9222877069545393219, -9222785464897897681, -9222198347540756780, -9221970424041518544]
- Table: app_events
  - Column: event_id
    - Samples: [2, 2, 2, 2, 2]
  - Column: app_id
    - Samples: [-8942695423876075857, -8022267440849930066, -5720078949152207372, -3725672010020973973, -1758857579862594461]
  - Column: is_installed
    - Samples: [1, 1, 1, 1, 1]
  - Column: is_active
    - Samples: [0, 0, 0, 0, 0]
- Table: app_events_relevant
  - Column: event_id
    - Samples: [2, 2, 2, 2, 2]
  - Column: app_id
    - Samples: [-8942695423876075857, -8022267440849930066, -5720078949152207372, -3725672010020973973, -1758857579862594461]
  - Column: is_installed
    - Samples: [1, 1, 1, 1, 1]
  - Column: is_active
    - Samples: [0, 0, 0, 0, 0]
- Table: app_labels
  - Column: app_id
    - Samples: [7324884708820027918, -4494216993218550286, 6058196446775239644, 6058196446775239644, 8694625920731541625]
  - Column: label_id
    - Samples: [251, 251, 406, 407, 406]
- Table: events
  - Column: event_id
    - Samples: [1, 2, 3, 4, 5]
  - Column: device_id
    - Samples: [29182687948017175, -6401643145415154744, -4833982096941402721, -6815121365017318426, -5373797595892518570]
  - Column: timestamp
    - Samples: [2016-05-01 00:55:25.0, 2016-05-01 00:54:12.0, 2016-05-01 00:08:05.0, 2016-05-01 00:06:40.0, 2016-05-01 00:07:18.0]
  - Column: longitude
    - Samples: [121.0, 104.0, 107.0, 104.0, 116.0]
  - Column: latitude
    - Samples: [31.0, 31.0, 30.0, 23.0, 29.0]
- Table: events_relevant
  - Column: event_id
    - Samples: [2, 6, 7, 9, 16]
  - Column: device_id
    - Samples: [None, None, None, None, None]
  - Column: timestamp
    - Samples: [-8942695423876075857, -8764672938472212518, -9050100410106163077, -7680145830980282919, -9142957261685295367]
  - Column: longitude
    - Samples: [1.0, 1.0, 1.0, 1.0, 1.0]
  - Column: latitude
    - Samples: [0.0, 1.0, 0.0, 0.0, 0.0]
- Table: gender_age
  - Column: device_id
    - Samples: [-9221086586254644858, -9221079146476055829, -9221066489596332354, -9221046405740900422, -9221026417907250887]
  - Column: gender
    - Samples: [M, None, M, M, F]
  - Column: age
    - Samples: [29, None, 31, 38, 31]
  - Column: group
    - Samples: [M29-31, None, M29-31, M32-38, F29-32]
- Table: gender_age_test
  - Column: device_id
    - Samples: [-9223321966609553846, -9223042152723782980, -9222896629442493034, -9222894989445037972, -9222894319703307262]
- Table: gender_age_train
  - Column: device_id
    - Samples: [-9223067244542181226, -9222956879900151005, -9222754701995937853, -9222352239947207574, -9222173362545970626]
  - Column: gender
    - Samples: [M, M, M, M, F]
  - Column: age
    - Samples: [24, 36, 29, 23, 56]
  - Column: group
    - Samples: [M23-26, M32-38, M29-31, M23-26, F43+]
- Table: label_categories
  - Column: label_id
    - Samples: [1, 2, 3, 4, 5]
  - Column: category
    - Samples: [None, game-game type, game-Game themes, game-Art Style, game-Leisure time]
- Table: phone_brand_device_model2
  - Column: device_id
    - Samples: [-9223321966609553846, -9223067244542181226, -9223042152723782980, -9222956879900151005, -9222896629442493034]
  - Column: phone_brand
    - Samples: [小米, vivo, 小米, 三星, OPPO]
  - Column: device_model
    - Samples: [红米note, Y19T, MI 3, Galaxy Note 2, A31]
- Table: sample_submission
  - Column: device_id
    - Samples: [-9223321966609553846, -9223042152723782980, -9222896629442493034, -9222894989445037972, -9222894319703307262]
  - Column: F23-
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: F24-26
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: F27-28
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: F29-32
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: F33-42
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: F43+
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: M22-
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: M23-26
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: M27-28
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: M29-31
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: M32-38
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]
  - Column: M39+
    - Samples: [0.0833, 0.0833, 0.0833, 0.0833, 0.0833]

SELECT AVG(T1.age) FROM gender_age AS T1 INNER JOIN events_relevant AS T2 ON T1.device_id = T2.device_id INNER JOIN app_events_relevant AS T3 ON T2.event_id = T3.event_id WHERE T3.is_installed = 1 AND T3.is_active = 0