Comprehensive design of an mHealth intervention protocol

With the aim of answering the first part of RQ4: “How can mHealth-based data-collection and analysis be combined with traditional research methods and measures to produce more comprehensive information about what, how and why changes occur during an mHealth intervention?”, Paper 4 [254]

describes a mixed-method study protocol. This protocol suggests a way in which an mHealth resource, app usage-logs, and mHealth approaches, e.g., the end user-driven structure of analysis, study, and intervention design, can supplement traditional methods and measures.

6.4.1 Intervention study design

The developed intervention was a system for sharing data between a patient’s app and their HCPs during consultations. It was designed and chosen because it allowed for both patient and HCP understanding and use as well as characteristics of measurement for the potential of mHealth: impacts on patients, HCPs and consultation structure. We designed a 6-month mixed-method feasibility study to measure these impacts. Before the study start, patients were sent a questionnaire both to their email and the Diabetes Diary app. Patients were encouraged to use the central app to collect data about their BG measures, medication doses, diet, weight, physical activity, and goals, in any manner that they choose. Monthly reminders sent as set follow-up messages through their email and app related to possible app functionalities to use etc. The follow-up message at month 5 instructed patients to schedule a consultation, during which they were meant to share their choice of collected data. HCPs were instructed to complete a post-consultation questionnaire after each time a patient shared data with them. Patients were then sent a study-end questionnaire after the research team had confirmed the HCP post-consultation questionnaire had been completed, thereby confirming the patients’

Figure 10 Illustration of what and when data was collected.

We chose the following standardized questionnaires, quantitative measures based upon a goal of measuring a balance of wellbeing, motivation, and health behavior change theories, and technological possibilities. Standardized questionnaires distributed at baseline (Appendix G) and 6-months

(Appendix H) measured patients’ perceptions of intervention usability (System Usability Scale –SUS) [234], own diabetes empowerment (Diabetes Empowerment Scale-Short Form- DES-SF) [235], own wellbeing (WHO-5 Wellbeing Index) [236], and the therapeutic relationship with their HCPs (Health Care Climate Questionnaire- HCCQ) [237].

Study-specific questionnaires allowed us to gather information that was specific to our research questions but were not part of the included standardized questionnaire. Appendix F includes the study-specific questions for patients at baseline, which were distributed with the aforementioned

standardized questionnaires. To measure the impacts of the data-sharing system on HCPs, we asked them to complete a pre-study survey (Appendix I) related to their impressions and expectations of the data-sharing system. After each consultation with a patient participant, we also requested that they complete a survey of which shared-data was discussed, which of the system’s functionalities were used (Appendix J).

Quantitative changes in patients’ health were measured by comparing the HbA1c and BP levels available in the medical records from lab tests. Usage-logs provided quantitative information about both what had changed by collecting own-gathered measures of health and self-management tasks.

These also demonstrated how these data had changed by demonstrating longitudinal patterns throughout the intervention in the logged data.

Study-end focus groups provided a means to collect more information about how participants acted during the intervention and why they chose to use the technology in the manner that was recorded via the usage logs. We invited both patients and HCPs to separate sessions. The meetings were designed to allow participants to elaborate on their questionnaire responses and provide more context for their perceptions of and actions during the intervention. The focus group discussion guides were the result of a joint effort of FullFlow Project team members and three psychology researchers. Questions were based upon psycho-social theories of behavior change that were not available or being used in

comparable studies (Appendix K) [231]. Questions were chosen that addressed the following: the traditional measure of technology interventions, i.e., user-experience, as well as users’ intentions, perceptions, and motivations related to using mHealth for sharing patient-gathered data during consultations, as well as patient-HCP collaboration (Appendix L and Appendix M). These were audio-recorded, transcribed, and translated from Norwegian to English for analysis.

6.4.3 Use of the mHealth study-administration platform

While it was possible for this platform to perform nine functionalities (Appendix N) [255]: 1) recruit, 2) send and collect informed consent, 3) randomize participants, 4) send and receive questionnaires, 5) track and 6) follow-up participants as they moved through the study, 7) gather and 8) provide basic summaries of data and 9) perform study closure. However, for this project, patient recruitment was performed through HCPs, who were themselves recruited in person, and randomization was not part of the study design.

Users within the research team would be able to access project information through a secure web-based interface. From here, it was possible to track participants as they moved through the study from informed consent to the reception of follow-up messages to the delivery and reception of

questionnaires. We initially created one follow-up message for each of the six months of the intervention as well as a study-end “Thank you” to ensure engagement and support was provided to participants. Each of these was sent and tracked manually through the platform.

It was then possible to access collected data from three data-sources: LimeSurvey [256], the app data server, and Piwik (now called Matomo [257]) for usage-logs. LimeSurvey also allowed us to create questionnaires for both patients and HCPs in the study. Within the FullFlow data-sharing system landing page, the HCPs could access the post-consultation questionnaire. Piwik automatically uploaded participants’ interactions with the app regularly, when they were connected to the internet.

The coordination of these platforms is illustrated in Figure 11.

Figure 11 Illustration of the flow of data from the participants to the research team using the study management platform in the FullFlow mixed-method study.