diff --git a/docs/change-log.md b/docs/change-log.md
index 06b12ef9b..02b188dc7 100644
--- a/docs/change-log.md
+++ b/docs/change-log.md
@@ -1,5 +1,13 @@
 # Change Log
 
+## v1.0.1
+- Fix crash in `chunk_episodes` of `utils.py` for multi time zone data
+- Fix crash in BT Doryab provider when the number of clusters is 2
+- Fix Fitbit multi time zone inference from phone data (simplify)
+- Fix missing columns when the input for phone data yield is empty
+- Fix wrong date time labels for event segments for multi time zone data (all labels are computed based on a single tz)
+- Fix periodic segment crash when there are no segments to assign (only affects wday, mday, qday, or yday) 
+- Fix crash in Analysis Workflow with new suffix in segments' labels
 ## v1.0.0
 - Add a new [Overview](../setup/overview/) page.
 - You can [extend](../datastreams/add-new-data-streams/) RAPIDS with your own [data streams](../datastreams/data-streams-introduction/). Data streams are data collected with other sensing apps besides AWARE (like Beiwe, mindLAMP), and stored in other data containers (databases, files) besides MySQL.
diff --git a/src/data/datetime/assign_to_multiple_timezones.R b/src/data/datetime/assign_to_multiple_timezones.R
index 8b78e47e4..f94cface2 100644
--- a/src/data/datetime/assign_to_multiple_timezones.R
+++ b/src/data/datetime/assign_to_multiple_timezones.R
@@ -148,7 +148,8 @@ multiple_time_zone_assignment <- function(sensor_data, timezone_parameters, devi
       mutate(data = map2(data, tzcode, function(nested_data, tz){
         nested_data %>% mutate(local_date_time = format(as_datetime(timestamp / 1000, tz=tz), format="%Y-%m-%d %H:%M:%S"))  
       })) %>% 
-      unnest(cols=everything())
+      unnest(cols=everything()) %>% 
+      ungroup()
   }
   
   tz_intervals <- buils_tz_intervals(data_tz_codes, device_type)
@@ -159,7 +160,8 @@ multiple_time_zone_assignment <- function(sensor_data, timezone_parameters, devi
       group_by(device_id) %>% 
       nest() %>% 
       mutate(data = map2(data, device_id, assign_tz_code, tz_intervals, device_type)) %>% 
-      unnest(cols = data)
+      unnest(cols = data) %>% 
+      ungroup()
   }
   
   return(sensor_data)
diff --git a/src/data/datetime/readable_datetime.R b/src/data/datetime/readable_datetime.R
index 79fc19d2e..1ac7c3f0e 100644
--- a/src/data/datetime/readable_datetime.R
+++ b/src/data/datetime/readable_datetime.R
@@ -61,8 +61,8 @@ create_mising_temporal_column <- function(data, device_type){
           mutate(data = map2(data, local_timezone, function(nested_data, tz){
             return(nested_data %>%  mutate(timestamp = as.numeric(ymd_hms(local_date_time, tz=tz)) * 1000) %>% drop_na(timestamp))
             })) %>% 
-          unnest(cols = everything())) %>% 
-          ungroup()
+          unnest(cols = everything()) %>% 
+          ungroup())
     } else {
       # For the rest of devices we infere local date time from timestamp
       if(nrow(data) == 0)
@@ -73,8 +73,8 @@ create_mising_temporal_column <- function(data, device_type){
           mutate(data = map2(data, local_timezone, function(nested_data, tz){
             return(nested_data %>%  mutate(local_date_time = format(as_datetime(timestamp / 1000, tz=tz), format="%Y-%m-%d %H:%M:%S")) %>% drop_na(local_date_time) )
             })) %>% 
-          unnest(cols = everything())) %>% 
-          ungroup()
+          unnest(cols = everything()) %>% 
+          ungroup())
     }
 }
 
@@ -120,11 +120,12 @@ readable_datetime <- function(){
     most_common_tz <- get_participant_most_common_tz(timezone_parameters$MULTIPLE$TZCODES_FILE, participant_file) # in assign_to_multiple_timezones.R
   }
 
-  output <- create_mising_temporal_column(output, device_type)
-  output <- split_local_date_time(output)
-  output <- assign_to_time_segment(output, time_segments, time_segments_type, include_past_periodic_segments, most_common_tz)
-  output <- filter_wanted_dates(output, participant_file, device_type)
-  output <- output %>% arrange(timestamp)
+  output  %<>%  
+    create_mising_temporal_column(device_type)  %>% 
+    split_local_date_time() %>% 
+    assign_to_time_segment(time_segments, time_segments_type, include_past_periodic_segments, most_common_tz) %>% 
+    filter_wanted_dates(participant_file, device_type) %>% 
+    arrange(timestamp)
 
   write_csv(output, snakemake@output[[1]])
 }