Hiring the Right Technical Support for Studies with Mobile Technologies: Key Questions and Strategies

Summary

Hiring the right person for any position can be challenging, but this is especially true for research teams involved in mobile health (mHealth) studies. These projects often require a unique skill set to handle data management and technical hurdles that differ significantly from traditional research support roles (Ben-Zeev et al., 2015). In this article, we will explore the necessity of hiring the right technical support person for your research team, what types of questions you should ask yourself before starting your candidate search, discuss options for reaching a talented candidate pool, and provide a list of common skills and competencies to aid in crafting job descriptions.

Importance of Hiring the Right Person

One of the major concerns that mHealth studies face is that of data security and participant privacy (Kotz et al., 2016; P. Galetsi, K. Katsaliaki, & S. Kumar 2023). Hiring an adept technical support person is one way of ensuring data is collected and managed securely (Aljedaani & Babar, 2021). This becomes especially critical if the prospective candidate will also develop or manage an mHealth-related app. In a review by Aljedaani and Babar (2021), lack of knowledge and training of mHealth app developers, and limited stakeholder involvement (e.g., clinicians, research staff, etc.) throughout the project, were identified as critical barriers to developing safe and secure apps.

Beyond safeguarding participant data, a well-qualified technical expert can prevent delays in moving from data collection to analysis. The sheer volume of data produced through mobile technologies is as exciting as it is daunting. The data are oftentimes messy and complex, requiring substantial pre processing before they are fit for analyses (Gökalp et al., 2018). Processing this big data is no small task and requires specialized knowledge and expertise to ensure data are handled accurately and efficiently (Gökalp et al., 2018). Many research statisticians, while highly proficient in data analysis, are not always equipped with the training, experience, or time needed to clean and organize data produced by mobile technologies. Without having the necessary technical support, research projects can find themselves stuck in limbo with vast quantities of uncleaned, unanalyzable data.

Questions to Ask Before Starting the Hiring Process

Now that we’ve established the importance of hiring the right person, the next step is to define the specific responsibilities and tasks this role will encompass. First and foremost is identifying the responsibilities and tasks that will be assigned to their role. Knowing what areas they will fill in your research efforts will help you to determine the types of skills and proficiencies that you should be looking for. Below, we will go over several guiding questions to help identify your specific needs for this position.

What types of technologies will they be working with?

Identifying which technologies your new hire will interact with is essential. If your study uses multiple devices, prioritize the technologies with which this person will have direct involvement. Once you’ve identified the technologies they will work with, note the specifics of each one. For example, if they will be working with mobile phones, will they need to understand iOS, Android, or both operating systems? Next, consider the data pipeline that will be used: where does the data originate, where is it processed and stored? Looking at the data pipeline will give you information about what data formats, software, or other tools your technical support person should be familiar with. For a list of common tools and software typically needed for working with mobile data, refer to the resources provided in this article.

What will their role be with these technologies?

A technical support person’s type and level of involvement with mobile technologies will inform you of the level of proficiency required for them to be successful in their role. It takes a substantially greater amount of software development knowledge to create an mHealth mobile application than it does to help participants troubleshoot app navigation issues. In the case of the former, an individual may require advanced or expert knowledge of software development, while the latter may only require intermediate proficiency. Sema’s Developer Skill Matrix is a helpful resource for assessing different levels of proficiency in software development. The Matrix is a reference table intended for developers to self-assess their own skill levels but can also be useful in identifying job requirements or evaluating potential hires more effectively (Van Itallie, 2022).

Will they need to manage and/or clean data?

As mentioned above, examining the data pipeline used in your study can help determine the types and levels of skills that this work will require. At what point in the pipeline will this technical support person encounter the data? If they come into contact with the data early on, or directly from the source, they will likely need experience dealing with large, complicated, and often multilevel, datasets. Will there be any need for automation to be set up for collecting, compiling, or managing this data? If this role will be responsible for the creation of functions meant to automate portions of the data pipeline, then they will require knowledge of one or more programming languages.

Will their work require knowledge of the subject matter?

Consider whether your technical support person will need to contribute to the scientific process itself by asking research questions or generating hypotheses. If their role is purely technical, this may not be necessary. Your team already has subject matter experts, but it’s likely you don’t have technical experts. According to other mHealth researchers, having both technical and health experts collaborating throughout the research process is necessary for achieving optimal outcomes (Ben-Zeev et al., 2015). Ensuring you have that technical perspective on your team is crucial. Focusing solely on technical skills –rather than both technical and health– will also expand your potential applicant pool.

Look for the right skills, not all skills

There is a wide array of competencies a technical support person may possess. While it can be tempting to want to find a “jack of all trades,” knowing which skills are essential versus those that are simply desired can affect your chances of finding the right candidate. Listing requirements in your job description that may not be needed can potentially discourage applications from talented individuals with more specialized expertise. Additionally, the number and type of qualifications listed will influence the salary expectations for the role. These details, along with labor market data, are used to determine the appropriate classification and pay range specific to your department. This is important to keep in mind if you’re working with budget constraints. You can utilize the Career Path Navigator to explore common job titles at the University and compare their descriptions to see which title best fits your needs. Since salary ranges aren’t included in the Navigator, you may need to coordinate with your unit’s Human Resources representative to determine which job classifications align with your budget.

Embracing Interdisciplinary Collaboration

We’ve already established that studies with mobile technologies have unique needs that differ from those of traditional health science studies. By its very nature, mHealth requires cross-disciplinary collaboration to function. As emphasized by Ben-Zeev et al. (2015), mHealth studies delve into areas unfamiliar to most clinical researchers, thus requiring input from technologists to help navigate issues in the digital space. From designing to maintaining and optimizing mobile technologies, each step in an mHealth project requires technical input.

This interdisciplinary collaboration requires teams to work across differences in methodologies stemming from clinical and technical perspectives. While traditional research focuses on evaluating effectiveness using methods such as the Randomized Control Trial (RCT), technologists favor methods similar to those used in engineering, focusing on testing and optimization prior to evaluation (Ben-Zeev et al., 2015). Strategically marrying these perspectives is needed for successful mHealth research. This can be achieved through utilizing methods such as the Multiphase Optimization Strategy (MOST).

MOST borrows from the field of engineering to describe an optimization phase of intervention development aimed at refining interventions to balance affordability, scalability, effectiveness, and efficiency, prior to conducting efficacy trials (Collins, 2018). Applying this optimization to mHealth interventions requires the use of tools designed with mobile technologies in mind, such as those developed by the Data Science for Dynamic Intervention Decision Making Center (d3center). However, successfully implementing these tools hinges on having a team with integrated data expertise– crucial for managing the complexity of such interventions.

But where do you find the technical savants necessary to build this kind of team? In most cases, you’ll need to look beyond your own field and explore disciplines such as information technology or software engineering. Again, this is where having clearly defined job responsibilities is paramount. You can reference these tasks to decide which technical-facing disciplinary backgrounds fit best with your needs. If you’re unclear which disciplines relate to the skills you’re looking for, you can get an idea by looking at the University’s course requirements for a corresponding degree. For example, the Master of Applied Data Science degree at the School of Information requires that students take a data manipulation course, which provides a foundation in data cleaning and management. This is helpful for knowing what backgrounds to look for in your potential candidates, but also for identifying current students or recent graduates that may thrive in a research support role. However, if you are considering using student technical support for your study rather than a traditionally hired candidate, it’s important to be aware of the differences between the two.

Student vs. Traditional Hire

Students can be invaluable, and come with certain benefits:

• Cost-effectiveness: Student workers are generally a more affordable option than traditional hires, sometimes requiring no financial support depending on their program.
• Fresh perspectives and innovative ideas: Students often bring new ideas and the latest knowledge from their coursework, which can be particularly valuable for mHealth studies where methodologies/technologies are constantly evolving.
• Enthusiasm and willingness to learn: Many students are eager to gain real-world experience and are enthusiastic about learning, which can make them highly motivated team members.
• Flexible and temporary: For short-term projects or intermittent needs, students provide a level of flexibility that may be ideal compared to traditional, longer-term hires.
• Potential for future recruitment: If a student excels, they may be a valuable candidate for longer-term or future roles after they graduate, providing a pipeline for recruiting early-career talent.

Despite these benefits, student support also comes with unique challenges and requirements that may affect whether this route is a viable option or not. When deciding if you should enlist student support versus hiring a traditional candidate, there are four main factors to consider: project goals, timeline, continuity, and staff capacity. Let’s go over some questions to ask yourself to help with determining if student support is the right option for your study:

Do the academic goals of the student align with project goals?

A student may have various academic requirements that don't always match the technical support needs of the study. For example, consider a project that requires using a specific data processing tool or pipeline to integrate app-generated data into a hospital’s electronic health record (EHR) system. In this case, the research team needs the data prepared in a format that is compatible with EHRs and compliant with strict security protocols. However, a student’s coursework may require them to explore a broad range of data cleaning or analytical tools, requiring them to use a particular software (such as SPSS or Excel) that doesn’t meet the technical needs of the hospital's system or the necessary privacy and security requirements. In this scenario, the student might prioritize gaining experience in the academically required tools to fulfill their class requirements, resulting in a dataset that is partially cleaned or incompatible with the project's real-world goals.

Does the project have a flexible or fixed timeline?

Students’ schedules are often complex and limited. Consider the time commitment related to the technical support needs of your study: are you working with a specific deadline or no set deadline? Most student projects are limited to a predetermined time frame which can range anywhere from a month to a full academic year. Additionally, students can generally only work a limited number of hours per week. If you think your project might require full time effort for timely completion, then student support is not the best choice.

Is there a clear documentation plan for continuity?

Students can produce amazing work, but this is only useful if it can be utilized and understood by others on the team. If a student performed data cleaning for your study but left no description of the methods and quality control measures they took, then the data might be entirely unusable, requiring repeat cleaning. This information is required for replicability, informing downstream analyses, and explanation in potential manuscripts. If you are planning to use student support, ensure that they document their work in real-time and share any code they have written, to enable others to understand what processes they used and to pick up where they left off if necessary.

Does the team have capacity for additional guidance and support?

Another limiting factor to using student support is the time commitment required from research staff. Students need more direction and reassurance than experienced staff, even if paid, which requires extra capacity from the research team. Many applied experience/internship opportunities for students even require that the team designate a primary contact who is available for regular meetings, providing feedback and communication, and evaluation of the student’s work. This can be made even more difficult if there is no one on your team with working hours that coincide with a student’s limited availability.

Recruitment resources

Using this knowledge, you can decide whether a student support is a good choice for your technical needs. If you find that your project aligns with the considerations listed above and you wish to onboard a student, then you can take advantage of the various student-to-research-team pipelines available across the university. Included below are just a few examples:

• UMSI Engaged Learning: The University of Michigan School of Information (UMSI) Engaged Learning Office (ELO), connects students with partners to provide them with applied experiences in a real-world setting. Partnering with ELO provides an opportunity to collaborate with UMSI students on technical projects. This partnership would work best for non-ongoing tasks that require basic technical skills and can be completed by a student in a reasonable amount of time. For example, a project that requires a moderately sized dataset to be cleaned and prepared before being handed off to a statistician for analysis.
• ITS Summer Internship Program: Information Technology Services (ITS) offers 14-week long full time summer internship opportunities for qualified students to work on IT-related projects. The program covers salary costs and provides each intern with a laptop. To qualify, a project must have clear deliverables, be able to be completed within 11 weeks, and can be done by interns with varying levels of experience. Job descriptions sought for this program include: Developer, Business Systems Analyst, UX Designer, System Administrator, Data Analyst, Project Manager, Communications/Social Media Specialist, Support Specialist, Technical Writer/Instructional Designer, Administration.
• UMSI Career Development Office: The Career Development Office can assist research teams in connecting with UMSI students or graduates looking for employment. They can aid in developing a customized recruitment strategy to best fit the needs of the role you’re looking to fill, matching skills to job requirements. This route is helpful for recruiting student workers and can also be used if your team is looking to hire UMSI alumni for a long-term or permanent technical support position.

Accessibility and Work-Life Balance

Knowing where to find the right talent is just the first step, the next major hurdle is knowing how to attract and retain qualified candidates. Keeping in mind best practices in hiring, and remaining cognizant of work-life balance, will greatly aid in this process.

Hiring best practices

We discussed previously the importance of job descriptions in identifying the right candidates, but this also plays a vital role in the accessibility of your posting. To increase your chances of finding talented individuals, you will want to ensure your hiring practices don’t inadvertently discourage certain individuals from applying. The Human Resources website for the university has a curated list of guides and resources available to aid in each step of the hiring process (UM login required for access): Resources for Hiring Departments

For help with crafting your job description, you can reference this example of a technical support role at the university: Example Job Posting

Work-life balance

The COVID-19 pandemic led to a substantial increase in the percentage of people working from home. This shift, while less pronounced, is still evident today. Due to the nature of their work, individuals working in IT and related careers have largely remained remote. Many workers in technical fields have become accustomed to working from home, and having that flexibility greatly impacts the desirability of a position. Keep this in mind when crafting your job description, as requiring in-office work is likely to discourage many applicants. Allowing for a flexible work schedule also aids in accessibility, opening the opportunity to a more diverse audience, and ultimately leads to a larger talent pool to choose from.

Common Skills and Competencies for Technical Support Roles

Below is a list of skills and competencies that are needed for working with mobile technologies.  The skills are categorized according to the various roles a technical support person may play, from database management to software development. This list is not exhaustive but is meant to serve as a starting point. You can use this as a guide to help build the job description for your technical support role:

• Databases and Data Formats:
  • XML
  • JSON
  • SQL
  • Oracle
  • MS SQL Server
• Data Pipelines, cleaning, transformations, ETL (extract, transform, load), ELT:
  • Apache Airflow
  • PowerQuery
  • Python
  • dbt
  • Informatica
  • SQL Server Integration Services (SSIS)
  • Tableau Prep
  • BizTalk
  • REST / SOAP
  • API development (client and server)
• Visualizations:
  • R
  • Power BI
  • Tableau
  • Looker
• Data Analysis:
  • R
  • SAS
  • EHR / FIHR data extraction
  • Data cleaning / prep / wrangling
  • Basic statistics knowledge
• Development:
  • Mobile app development
  • Smartwatch app development
  • Responsive web development
  • HTML
  • CSS
  • JavaScript
  • ASP.NET
  • C / C# / C++
  • Java
  • Python
  • Algorithm development
  • Machine learning / model training
  • UI / user experience design
  • Agile methodology
  • Software development life cycle
  • Requirements gathering
  • Advanced MS Excel
• Automation and Workflows:
  • PowerAutomate
  • SharePoint
  • TDX iPaaS
  • Automation Anywhere
• Mobile Technologies Specific:
  • Mobile and time-series data cleaning
  • APIs for Garmin, Apple HealthKit, Apple SensorKit, Fitbit, Samsung Health, Google Fit, Google Health, Withings, Polar, Omron, Dexcom, Freestyle Libre
  • Knowledge of clinical research process
  • Experience with mobile data platforms such as MyDataHelps, mEMA, Fitabase
• Other:
  • EHR / FIHR data extraction
  • Experience working with healthcare or health insurance data

Resources

1. Tools of the Trade for Mobile Data - https://teamdynamix.umich.edu/TDClient/210/DepressionCenter/KB/ArticleDet?ID=10742
2. Developer Skills Matrix - https://github.com/Semalab/developer-skills-matrix/blob/main/rubric/rubric.md
3. UMSI Engaged Learning Office - https://www.si.umich.edu/student-experience/engaged-learning
4. ITS Internship Program - https://internship.its.umich.edu/home
5. UMSI Career Development Office - https://www.si.umich.edu/student-experience/career-development
6. UM Resources for Hiring Departments (umich login required to access) - https://hr.umich.edu/working-u-m/management-administration/talent-acquisition/resources-hiring-departments
7. Technical Support Job Description Example

References

1. Aljedaani, B., & Babar, M. A. (2021). Challenges With Developing Secure Mobile Health Applications: Systematic Review. JMIR mHealth and uHealth, 9(6), e15654. https://doi.org/10.2196/15654
2. Ben-Zeev, D., Schueller, S. M., Begale, M., Duffecy, J., Kane, J. M., & Mohr, D. C. (2015). Strategies for mHealth research: lessons from 3 mobile intervention studies. Administration and policy in mental health, 42(2), 157–167. https://doi.org/10.1007/s10488-014-0556-2
3. Collins, L.M. (2018). Optimization of Behavioral, Biobehavioral, and Biomedical Interventions. Springer. https://doi.org/10.1007/978-3-319-72206-1
4. Galetsi P., Katsaliaki K., & S. Kumar S. (2023). Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists. Technovation, 121. https://doi.org/10.1016/j.technovation.2022.102598.
5. Gökalp M.O., Kayabay K., Akyol M.A., Koçyiğit A., Eren P.E. (2018). Big Data in mHealth. In E. Sezgin, S. Yildirim, S. Özkan-Yildirim, & E. Sumuer (Eds.), Current and Emerging mHealth Technologies: Adoption, Implementation, and Use (pp. 241-56). Springer International Publishing.  https://doi.org/10.1007/978-3-319-73135-3_15
6. Kotz, D., Gunter, C. A., Kumar, S., & Weiner, J. P. (2016). Privacy and Security in Mobile Health: A Research Agenda. Computer, 49(6), 22–30. https://doi.org/10.1109/MC.2016.185
7. Van Itallie, M. (2022). Measurable and meaningful skill levels for developers. Stack Overflow. https://stackoverflow.blog/2022/07/28/measurable-and-meaningful-skill-levels-for-developers

About the Author

Isabelle Birt, MPH is a Communications Assistant at the d3center within the University of Michigan's Institute for Social Research. Isabelle works closely with the Creative Lead designing visual and written resources to share the center's innovative research to a broad audience. She has provided data support for studies within the Michigan Neuroscience Institute and Michigan Medicine's Cardiology Department, cleaning and managing large molecular expression datasets.   |   |