diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 42a42a725..f495df826 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -56,6 +56,7 @@ Detailed list of changes
 
 - When anonymizing the date of a recording, MNE-BIDS will no longer error during `~mne_bids.write_raw_bids` if passing a `~mne.io.Raw` instance to ``empty_room``, by `Daniel McCloy`_ (:gh:`1270`)
 - Dealing with alphanumeric ``sub`` entity labels is now fixed for :func:`~mne_bids.write_raw_bids`, by `Aaron Earle-Richardson`_ (:gh:`1291`)
+- When processing subject_info data that MNE Python imports as numpy arrays with only one item, MNE-BIDS now unpacks these, resulting in a correct participants.tsv, by `Thomas Hartmann`_ (:gh:`1310`)
 
 ⚕️ Code health
 ^^^^^^^^^^^^^^
diff --git a/mne_bids/tests/test_write.py b/mne_bids/tests/test_write.py
index 63485cda2..e1f369744 100644
--- a/mne_bids/tests/test_write.py
+++ b/mne_bids/tests/test_write.py
@@ -4187,3 +4187,48 @@ def test_write_evt_metadata(_bids_validate, tmp_path):
     for cur_col in event_metadata.columns:
         assert cur_col in events_tsv
         assert cur_col in events_json
+
+
+# XXX: Remove once MNE-Python <1.9 is no longer supported
+@testing.requires_testing_data
+def test_write_bids_with_age_weight_info(tmp_path, monkeypatch):
+    """Test writing participant.tsv when using np.arrays for weight and height."""
+    bids_root = tmp_path / "bids"
+    raw_fname = data_path / "MEG" / "sample" / "sample_audvis_trunc_raw.fif"
+    raw = _read_raw_fif(raw_fname)
+    # disable MNE-Python 1.9+ validation for the duration of this test
+    if "subject_info" in getattr(mne.Info, "_attributes", {}):
+        monkeypatch.setitem(
+            mne.Info._attributes,
+            "subject_info",
+            lambda val, **kwargs: val,
+        )
+    raw.info["subject_info"] = {
+        "weight": np.array([75.0]),
+        "height": np.array([180.0]),
+    }
+
+    bids_path = _bids_path.copy().update(root=bids_root, datatype="meg", run=1)
+    write_raw_bids(raw, bids_path=bids_path)
+    bids_path = _bids_path.copy().update(root=bids_root, datatype="meg", run=2)
+    write_raw_bids(raw, bids_path=bids_path)
+
+    # Test that we get a value error when we have more than one item
+    raw.info["subject_info"] = {
+        "weight": np.array([75.0, 10.2]),
+        "height": np.array([180.0]),
+    }
+
+    bids_path = _bids_path.copy().update(root=bids_root, datatype="meg", run=3)
+    assert_array_equal(raw.info["subject_info"]["weight"], [75.0, 10.2])
+    with pytest.raises(ValueError, match="more than one element"):
+        write_raw_bids(raw, bids_path=bids_path)
+
+    # Test that scalar data is handled correctly
+    raw.info["subject_info"] = {
+        "weight": 75.0,
+        "height": np.array([180.0]),
+    }
+
+    bids_path = _bids_path.copy().update(root=bids_root, datatype="meg", run=3)
+    write_raw_bids(raw, bids_path=bids_path)
diff --git a/mne_bids/write.py b/mne_bids/write.py
index b7cf34d02..500d300ff 100644
--- a/mne_bids/write.py
+++ b/mne_bids/write.py
@@ -511,6 +511,29 @@ def _participants_tsv(raw, subject_id, fname, overwrite=False):
         }
     )
 
+    # XXX: Remove once MNE-Python <1.9 is no longer supported
+    # Make sure that all entries to data are lists that
+    # contain scalars (i.e. not further lists). Fix if possible
+    for key in data.keys():
+        cur_value = data[key]
+
+        # Check if all values are scalars
+        new_value = []
+        for cur_item in cur_value:
+            if isinstance(cur_item, list | tuple | np.ndarray):
+                if len(cur_item) == 1:
+                    new_value.append(cur_item[0])
+                else:
+                    raise ValueError(
+                        f"Value for key {key} is a list with more "
+                        f"than one element. This is not supported. "
+                        f"Got: {cur_value}."
+                    )
+            else:
+                new_value.append(cur_item)
+
+        data[key] = new_value
+
     if os.path.exists(fname):
         orig_data = _from_tsv(fname)
         # whether the new data exists identically in the previous data