Development and Validation of a Novel Self-assessment System Based on a Mobile App to Manage Adult Growth Hormone Deficiency (MAGHD App): a Single-Centre Model. GrASS (Growth Hormone Deficiency in Adults Self-assessment System)

AGHD is widely recognized as a specific clinical entity and recombinant human growth hormone (r-hGH) therapy has become a standard practice since 90s thanks to its beneficial effects on body composition, muscle strength, bone mass, cardiovascular risk and patients' exercise capacity and QoL. All the beneficial effects of r-hGH in AGHD patients have been established mainly on selected populations by both few randomized controlled trials (RCTs) and several clinical trials. Real life studies are also available, but they are mainly based on data collected through multicenter sponsored studies such as the Pharmacia & Upjohn International Metabolic Database (KIMS) and the Hypopituitary Control and Complications Study International Advisory Board (HYPOCCS). All these observational studies suffer from missing data in the original databases and/or lack adequate control group (i.e. untreated GHD patients). Well-conducted real life studies have the advantage to provide information on long-term effects of therapies, as well as on safety, adherence and persistence to therapy. Accordingly, in clinical practice it is common to find a disparity between hormonal levels and patients' subjective well-being: some individuals with a full-blown AGHD do not report any symptom and turn out reluctant to start a treatment, while others with slight GHD experience a deterioration in their QoL that deeply improves after GH replacement. This is mainly due to the fact that clinical presentation of AGHD is frequently based on nonspecific symptoms. Similarly, individual differences in responsiveness to r-hGH are common findings in the real life. Moreover, when other hormonal deficits coexist and patients have to face with a polytherapy, it can be difficult both for clinicians and patients to relate symptoms to the sole AGHD and to verify benefits after treatment. Finally, there is a lack of tools assessing patients' physical activity, which is an important factor to consider when evaluating energy levels, exercise performance, and well-being. Thus, a gap still remains in the real life setting concerning how to measure the improvements occurring in r-hGH treated patients and how to make patients and clinicians more aware of these changes. Nowadays, considering the shortage of established predictive factors for the overall therapeutic response, the decision to whether or not continue treatment depends often on the ratio of perceived and expected benefits over cost and risks of treatment, as well as on the persistent motivation of the patient. These aspects involve also physician's attitude and patient's propensity to start r-hGH replacement therapy. Vice versa the lack of information useful to predict patients' individual response to treatment poses some clinical troubles about how to select patients who may beneficiate from r-hGH therapy. Considering that r-hGH treatment is a chronic therapy requiring a daily self-injection, long-term compliance is still a considerable problem in this setting and is influenced by all the above mentioned issues. It is estimated that about 20 to 30% of patients discontinue the treatment, permanently or for extended periods, making therefore an objective evaluation of the therapeutic effects difficult. All these aspects are enhanced by the paucity of tools available to improve patients' education and awareness of AGHD.

The widespread use of Smartphone applications and wearable devices presents an opportunity to overcome these difficulties in the AGHD management. Although several apps exist to monitor chronic endocrinological diseases, no specific app has been tested in AGHD till now.

In a prospective, 24 months, open-label, monocentric study, an interactive smartphone app (MAGHD App) will be developed, connected to an Integrated Framework (MAGHD Framework) and tested by automatic software testing procedures before the real validation on patients. Subsequently it will be validated on an estimated number of 100 AGHD patients; this system will allow to combine patients' information and HCPs clinical outcomes in a single virtual platform and to return easily accessible data to both AGHD patients and clinicians.

The MAGHD App and the integrated MAGHD Framework will be provided by DataRiver, the research and technological Partner of the study, which is a Contract Research Organization certified by AIFA (Italian drug agency) and accredited as a Research Innovation Institution by the Emilia Romagna Region. The MAGHD software framework will allow integrating patient's Physical Activity data (PA-D), constantly collected by wearable devices (which will be rented and given to patients in the second part of the study), with patient related outcomes data (ePRO-D) and HCP data (HCP-D):

• PA-D will be obtained through wearable devices and will guarantee the automatic collection of data regarding patients' daily life activities. PA recorded by the wearable devices will include: physical activity type, daily steps number, daily walking distance, daily calories burned, sleep duration, sleep quality.
• ePRO-D will be collected via the MAGHD App, which will periodically generate a short question extrapolated from the items of validated questionnaires: AGHDA questionnaire and QLS-H questionnaire for QoL, the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) for well-being evaluation, the International Index of Erectile Function Questionnaire (IIEF-15) and the Female Sexual Function Index (FSFI) for sexual function evaluation (in men and women respectively), the International Physical Activity Questionnaire (IPAQ) for physical activity evaluation and the Pittsburgh Sleep Quality Index (PSQI) for sleep quality evaluation. Additional information on sexual function, working environment, hobbies and adherence to r-hGH therapy (the latter only in Group 1) will be collected through a simple series of questions taken from a specific chart.