The Few Touch application was tested for 1 year by 12 individuals with T2DM (4 men and 8 women; age ranged from 44 to 70 with a mean age of 55.1 (SD: 9.6) and mean disease duration was 8.1 (SD 3.8) years at the beginning of the long-term trial) who had been involved in the design process. We use the term “participants” for these 12 individuals. The local regional ethical committee approved the study protocol in 2006 (Regional komité for medisinsk forskningsetikk Nord, Ref. No. 13/2006). The recruitment process and other details about the participants are explained elsewhere [41].

The main component of the Few Touch application is the smartphone-based “Diabetes Diary”. Core features of the Few Touch application are: (1) automatic wireless data transmission from a blood glucose meter and a step counter, (2) nutrition habit recording enabled by few-touch operation on the smartphone, (3) feedback with simple analysis of these three types of data shown by the Diabetes Diary, (4) goal-setting functions for step counts and nutrition habits, and (5) general tips function for self-management of diabetes.

Figure 1 shows the structure and screenshots of each page in the Diabetes Diary. The “Phone (tlf)” button switches to the default top menu of the smartphone. Tapping the button “Angi Tidsrom” (change period) on screen (d) displays a blood glucose measure graph showing all the data for the set duration. Tapping “lav karb. (low carb.) snacks”, icons for meals, or the “status” button displays page (f). Tapping “høy karb. (high carb.) snacks” or icons for drinks displays page (g). Details of each function and the design process of the Few Touch application including reasons for the choice of technologies can be found elsewhere [41,42,44-47].

The schedule for the long-term trial and the timing of data collection is summarized in Table 1. At the introduction of the Few Touch application in September 2008, the authors explained that the frequency of using the system was up to the participants. Instead of providing detailed instructions for using the nutrition habit recording system, we challenged participants to record what they ate and/or drank in a way that was relevant for them, so that this process could evoke reflective thinking [48]. Due to the limited battery life of the step counter and the limited human resources after the end of the 6-month trial planned initially, all the step counters stopped working before the meetings held in October 2009.

Results from the Mann-Kendall trend test on usage rates confirmed a significantly decreasing (P<.05) usage trend among 10 out of the 12 participants, though the magnitude of the decrease varied widely (Table 2). The generally decreasing trend is in line with the result for blood glucose measurements found previously [43]. However, the analyses of datasets by participant revealed that some of the participants used functions constantly and in a very determined way during the trial period. Table 3 summarizes the numbers of days on which each function was used against a period during which each function was available. Both Table 3 and trends in usage rates (Multimedia Appendix 1) show that P03 and P09 used both the blood glucose sensor system and the physical activity sensor system very consistently.

Figure 2 shows the distribution of blood-glucose measurement frequency among days on which any blood glucose measurement was performed. From Figure 2, it is clear that P03 and P09 measured once a day for most of the days on which they had measurements (370 out of 390 days (95%) for P03 and 297 out of 348 days (85%) for P09). On kernel density estimates of distributions of time points at which blood glucose was measured (Multimedia Appendix 2), a significant single peak can be identified in the morning time for P03 and P09, which demonstrates that they are habituated to measure blood glucose in the morning almost every day. Including P03 and P09, a significant peak in the morning time was observed among most of the participants, which is also in line with the result from aggregated data for all the participants [43]. However, the kernel density estimates (Multimedia Appendix 2) also revealed that the patterns of times at which measurements were taken were quite different from one participant to another, which reflects different needs for blood glucose measurements. For example, P06 and P10 used the blood glucose sensor system for more than 70% of days when the function was available, and often they measured blood glucose more than once a day (Figure 2). From the kernel density estimates (Multimedia Appendix 2), it is observed that P10 measured in the morning more or less regularly and otherwise rather sporadically, while P06 usually measured very sporadically. P01 used the function moderately: once a day for most of the times when used (75%, 76 out of 102 days) (Figure 2). The two sharp peaks for P01’s density estimates (Multimedia Appendix 2) indicate that P01 often measured either in the morning or in the evening.

Regarding nutrition habit recordings, we could observe a change in usage patterns for some participants (eg, P01, P07, and P11) in a relatively early phase (Multimedia Appendix 3). This corresponds to what 4 participants (P01, P05, P06, and P11) told us at Meetings 2 and 3: they tried to record the data right after eating, but sometimes they recorded it at the end of the day to summarize their food intake, while P07 shifted recording activity to morning time. After updating the user interface of nutrition habit recording at Meeting 4, which is explained in detail in a later subsection, P08 clearly changed his/her way of nutrition-habit recordings from “right after every eating/drinking” occurrence to “summarizing at the end of the day”, whereas this modification did not seem to influence either usage patterns or usage rate for the other participants.

Regarding the physical activity sensor system, 9 (P01, P02, P03, P05, P07, P08, P09, P10, and P11) of the 12 participants had problems and their step counters were repaired or replaced. The major problem was battery attrition of the step counter. A significant decreasing usage trend (P<.05) was observed among 5 of the participants, which is less than for the other two functions. However, this result may need to be interpreted carefully due to the much shorter period in which the physical activity sensor system was available than the other two functions.

The Few Touch application was generally perceived as satisfactory. The perceived usefulness of the whole application by the participants remained considerably high over time (Table 4), though several of the participants expected the frequency of usage to decrease over time (Multimedia Appendix 4, Questionnaire 6). The average score for the SUS questionnaire was 84.0 (SD: 13.5, range: 67.5-100) [41,42]. This score is high compared with other studies where the SUS questionnaire was used, considering the average score together with the conclusion on usability in each study [55-57]. The result from Questionnaire 1 shows that only one user expressed dissatisfaction about the size of the mobile phone, which is 107 x 55 x 16 mm and weighs 120 gram. No other items were perceived as explicitly unsatisfactory for the Few Touch application in this questionnaire.

The blood glucose sensor system was perceived as the most motivating, followed by the physical activity sensor system and the nutrition habit recording system (Multimedia Appendix 4, Questionnaire 2). In contrast, the participants expressed lukewarm perceptions regarding the effect of using the Few Touch application on achieving adequate frequency of blood glucose measurement (Multimedia Appendix 4, Questionnaire 3). Likewise, only half of the participants answered that their blood glucose control had been improved, while slightly more participants answered that their physical activity level and nutrition habits had been improved in the course of 1 year (Multimedia Appendix 4, Questionnaire 9). No participants answered that their medication had been improved, but P03 (at Meeting 3) and P11 (at Meeting 4) mentioned in an interview and a focus group, respectively, that the number of tablets had been reduced. Also, overall satisfaction levels with their knowledge about diabetes and with skills in diabetes management were not drastically changed in the course of 1 year (Multimedia Appendix 4, Questionnaire 5). This is also reflected by the fact that participants’ satisfaction level with the tips function decreased over time (Multimedia Appendix 4, Questionnaire 7). Although the participants considered the tips concise and useful, 5 participants (P01, P03, P05, P09, and P12) suggested improvements, as reported previously [42].

The results of the questionnaire that addressed the participants’ preferences for potential functionalities of the Few Touch application are shown in Multimedia Appendix 4, Questionnaire 8, ranked by the total score. “A smaller step counter that is easier to wear” was highest rated, and “automatically popping-up tips” ranked as second. The other items that no one disagreed with were “automatic” functions. Besides “use of own mobile phone”, all items that were disliked by some of the participants were functions that involved communication with other people. Most of the participants stated that using the application as a “self-help” tool was enough for them, though collaborative use of the application might be motivating.

We could not observe any deterministic associations between answers to any questionnaires and usage of functions.

Together with qualitative inquiries, detailed quantitative analyses on each participant’s usage of the provided tool gave us insights into mechanisms of participants’ engagement with the tool and led us to a better understanding of factors associated with usage and usability over time.

The Few Touch application served as a flexible learning tool for the participants depending on their spontaneous needs as well as for regular self-monitoring. Usage of the application was supported by the minimum effort required for keeping track of self-management activities and for referring to them, which was the design concept achieved in the user-involved design process. Except for a few participants, a decrease in the usage trend was generally observed. Having gained a sense of mastery over diabetes and experiences of problems were identified as reasons for decreased motivation to continue using the application. Some of the problems stemmed from a mismatch between design concepts and reality, even though the design concepts were obtained in a process involving the participants. The impact of such mismatches on usage and usability became critical over time among some participants.

In the following sections, we will discuss our findings by comparing them with relevant studies.

The learning process based on personal experiences on top of necessary knowledge provided by diabetes education builds a foundation for designing the patient’s own self-management plan. It is also claimed in previous studies that a supporting tool for people with diabetes should facilitate this learning process [48,58]. The application was perceived as easy and simple. Such characteristics played an important role not only in enhancing motivation to use the application but also in the learning process because “keeping track of performed actions in self-management activities is notoriously difficult” [48].

A wide variety of patterns of engagement with the application observed in this study is in line with findings in the two deployment studies of a health monitoring application, MAHI (Mobile Access to Health Information). MAHI was originally designed with the focus on development of reflective thinking skills through social interaction for newly diagnosed individuals [59]. The first deployment study [27] for newly diagnosed patients revealed that many participants considered that intensive and focused exchange with the educator by using MAHI would be “most beneficial at the beginning of their engagement with diabetes management”. However, their second deployment study [60] confirmed that MAHI was also well accepted by people with more extensive diabetes experience as a tool for identity construction through storytelling. Consistent with the findings from the MAHI deployments, our study support the idea that design that allows divergent interpretation supports flexible appropriation [61]

Decrease in usage after gaining a sense of mastery over diabetes is in line with findings by relevant studies [30,32,62]. The application was equipped with neither “push” factors nor an advanced decision support system providing feedback based on accumulated data and other personal profiles, which are advocated for long-term usage, according to relevant studies [62-65]. Nevertheless, usage by many of the participants remained relatively high over the 1-year trial period (see Multimedia Appendix 1). This might have been caused by intrinsically high motivation of participants in the design process. As shown in Multimedia Appendix 4 Questionnaire 8, the participants expressed preferences for such features, suggesting the role these would play for stronger engagement with the application.

This study confirmed the importance of involving “patient-users”, not only in the specification-design phase but also in the trial phase of a working prototype in a real-life setting for enough time to clarify how the chosen design works in relation to expectations and how the design can be improved [40]. Mismatches between design concepts and reality can happen even though the same participants are involved in both design-concept making and a trial. Their impact on usability and usage may become critical over time. When technical or financial constraints hinder realization of obtained design concepts through patient-user involved processes, such as our case of step counters and choice of a smartphone model, it is especially important to examine how great the impact of mismatches would be on both usability and usage in the long term. Therefore, if possible, it is wise to continue involving the same patient-users so that effective feedback will be gained during the final cycle of design iterations.

To achieve such involvement of patient-users requires their strong and long engagement in the process. The strong engagement of the participants in this study was achieved through a variety of efforts by the researchers [42]. Among them, offering frequent opportunities to meet played an important role in motivating the participants to stay in the study, as it is one of the factors influencing nonusage attrition and dropout attrition in eHealth trials [33]. Frequent meetings also facilitated quick responses to the participants’ feedback by improving the prototype and by organizing new inquiries for further iterative design processes. The participants could feel that they were really contributing to the designing process through these interactive processes. Another advantage is that frequent meetings with other participants offer opportunities for them to learn from others. This was mentioned by some of the participants. Fudge et al [73] also found that learning from others in similar situations was one of patients’ motives to attend meetings in a user-involved program for improvement of health services for people with stroke. They discuss this issue as a concern that the ability of user involvement to improve service may be questionable, “if this (to improve service) is not the primary motivation of those involved”. In our study, we did not specifically investigate whether “learning from others” was the primary motivation for the participants to become involved in the design process or to attend the meetings. However, as far as we observed the participants in the meetings, this was a secondary effect in enhancing motivation to continue using the system.

Though the first author of this paper understands the Norwegian language, she is neither a Norwegian citizen nor a native Norwegian speaker. On the other hand, the second author is a native Norwegian, has T1DM himself, and has the same cultural background as the participants. In addition, the second author initiated the whole design process of the Few Touch application, from the recruitment of the participants to the development and testing of the application. The first author needed some help from the second author to interpret and understand better exactly what the participants meant in interviews. This might have caused a certain bias in the analysis.

The present study showed the importance of the following two factors: (1) a thorough analysis of results from multiple types of investigations focusing on each participant’s engagement with the tool over time, and (2) involving patient-users from an early phase of design-concept making to a longitudinal trial of the system. The value of these factors was shown by the ability to identify factors that influence usability and usage in real-life settings in a long-term perspective in relation to original design concepts.

The extent to which our findings can be generalized is limited by intrinsically high motivation among many of our participants as well as the small number of the participants. However, it is highly probable that the factors that this study identified as reducing usability or usage would do so when users were less motivated. A revised version of the Few Touch application was tested by a group of 11 patients with T2DM who were not involved in the design process in order to assess the validity of our findings, and it is now being used as an intervention tool in an ongoing randomized controlled trial [74]. In that trial, a quantitative analysis needs to be carried out to investigate association between perception of features of the Few Touch application, level of engagement with the application, and the primary and secondary outcomes. Although the participants in this study are no longer engaged in design process, the Few Touch application is evolving through many research projects in which new functions are implemented and feedback for improvement is given [75]. Such series of studies will also provide useful insights into factors associated with usage and usability of a mobile self-management system.