SensE-MSensing Physical Activity to Evaluate and Monitor a Routine Aftercare Program (SensE-M) - an Observational Study

Primary hypothesis (hypothesis 1 - effective integration): The investigators hypothesize that integrating the Fitbit effectively into the program to measure activity goals will support program participants in pursuing their daily activity goals, particularly, once they have returned home. A prerequisite for the Fitbit's effective integration into individuals' daily lives will be the definition of activity goals which can be measured and tracked conveniently with the Fitbit. Analysis plan: The analysis strategy will depend on the final sample size. Using a descriptive approach, the investigators will compute the difference between average daily activity after their return home and their predefined activity goals which have been defined as part of the 'Bliib dra'-program (e.g., active zone minutes of different intensity, steps per day) relative to previously agreed activity level goals. Furthermore, the investigators will examine participants' free text replies concerning challenges and facilitators in pursuing their activity goals in daily life and potential/difficulties of activity trackers in this regard. To extract relevant information, the investigators will evaluate Fitbit-related statements using natural language processing techniques. Secondary hypothesis (hypothesis 2 - daily-life activity at home): Further, the investigators hypothesize that program participants will maintain a consistent and relatively stable level of physical activity. Analysis plan: The analysis strategy will depend on the final sample size. The investigators will examine different activity level outcomes (e.g., active zone minutes, step count, high- /medium-intensity minutes) the time series data using descriptive and visual methods. If the sample size allows for more complex models, they will model physical activity over time and explore individual level-factors using a (multilevel) regression framework whereby controlling for individual-level factors. The investigators will investigate decline in activity levels defined as abrupt decrease or a steady decrease over an extended period of time (i.e., at least a week). Exploratory: The investigators will further explore what challenges program participants and therapists experience, what they appreciate, and what they need to effectively integrate activity trackers such as the Fitbit device effectively into routine care program 'Bliib dra'. They will also explore how individual-level health measures (e.g., PROMIS-10) change over time. Analysis plan: The investigators will examine therapists' and program participants' replies to the open questions using natural language processing techniques. With regard to the time series data, the analysis strategy will depend on the final sample size. They will examine the time series data using descriptive and visual methods. If the sample size allows for more complex models, the investigators will use a multivariable regression framework whereby controlling for individual-level factors.