Persisting challenges remain with regard to the time spent to formulate and write the feedbacks and to the implementation of the technology. According to the therapists’ evaluation in the CWP and http://www.selleckchem.com/products/XL184.html the T2DM studies, the average time to
write a tailored feedback was about 10–15 min, with substantially more time used on the initial feedbacks given. The therapist reported that using information and text segments (for example mindfulness exercises) from earlier feedbacks made the feedback process more time efficient [8] and [22]. It may be possible to develop a coding system for the different kinds of feedbacks the therapist wants to give and to let the therapist select suitable, more or less standardized feedback messages from pre stored examples. Modifications should then be made to adjust the feedback to each patient’s special needs. To utilize the technology resources even more, it could be possible to use the diary
input to automate the feedback from a registered databank. This databank could be automatically extended with new feedbacks given for specific situations, and a “self-learning” data system could be developed taking the results of each feedback into account. The patients reported that personalized feedback was important. It is therefore essential to find a balance between automating the process and making it more effective while taking into account the relevance of giving selleck inhibitor personalized feedback to the patients. These new developments result in a new type of intervention, requiring a new round of studies on efficacy and feasibility. Automation of the feedback is one way of making the intervention more time efficient. Another timesaving action could be to give weekly feedbacks instead of daily ones. In the diabetes project the feedbacks were given daily for 4 weeks and weekly for 8 weeks. Although the patients preferred the daily feedbacks they became used to the weekly feedbacks and continued to fill in the daily diaries as before. This indicates that the web-based intervention could be used to maintain adherence to the treatment and thereby achieve the effects with less effort.
Further studies are needed to analyze the effects of automation and reducing the feedback intervals. Although there was some variation over the three studies, adherence to find more the intervention protocol was not a big problem for the patients, at least not after the startup period. This may be related to the therapist’s commitment. Demotivated professionals are recognized as an adherence barrier [36]. De Veer and colleagues also analyzed factors which impede or enhance the successful implementation of new technologies in nursing care among potential users. The factors most frequently mentioned as impeding actual use were related to the technology itself, such as malfunctioning, ease of use, relevance for patients and risks to patients.