From Research to Reality

Ask someone how often they check social media and they'll tell you with reasonable accuracy. Ask a smartphone usage questionnaire to predict actual device behaviour and it fails entirely.

That's the measurement gap. And it's the reason most digital wellbeing research may be building on unreliable foundations.

The data

We recruited 82 iPhone users aged 18 to 30. They completed validated questionnaires and provided screenshots of their actual Screen Time data. When we compared what people reported to what they actually did, a pattern emerged.

What this means

Not all self-report measures are equal. When you ask people specific questions about frequency of social media use, their answers track reality. But when you use a broader smartphone usage scale, the relationship breaks down.

This measurement validity gap has implications for the whole field:

• The instruments matter more than we assumed. Researchers choosing between usage scales are making a consequential methodological decision. Some scales predict behaviour. Others don't.
• Specificity beats generality. Asking about social media frequency works. Asking about general smartphone use with a composite scale doesn't. This is consistent with Andrews et al. (2015) and Ellis et al. (2019) on the gap between self-reported and logged device use.
• If we can't measure use accurately, we can't measure impact accurately. Any intervention study built on unreliable baseline measurement is compromised from the start.

beò's approach

This research shaped beò's founding principle: if you want to understand technology's impact, you need instruments that actually measure what they claim to measure. That means:

This isn't just another app telling you to use your phone less. It's a research study built on the lesson that measurement validity comes first.

The hypothesis we're testing

Closing the perception gap, combined with evidence-based interventions, helps users make informed choices about their technology use.

The method

Everything we build is grounded in published research:

• Digital Flourishing Scale for baseline and outcome measurement (Janicke-Bowles et al., 2023)
• 28 evidence-based interventions across 7 categories: breathe, move, sense, light and air, nature, connect, focus
• Perception gap tracking (estimated vs actual screen time)
• Anonymous data collection guided by BPS ethics principles

Launch February 2026. First findings Easter 2026.

Why this matters now

99% of 18 to 24 year olds in the UK have a smartphone. 93% have at least one social media profile. This isn't a niche issue, it's the baseline human experience for an entire generation.

Yet most research still focuses on finding pathology: addiction, anxiety, depression. As Davidson and Ellis (2019) called it, "technological déjà vu", the same moral panic that accompanied television, video games, and every new technology.

We need research that starts from a different place: What actually helps people use technology in ways that align with their values?

Not abstinence. Not shame. Not taking away control. Understanding, then choice.

From academic to applied

beò is what happens when academic research meets enterprise engineering. I spent 30 years building technology for Fortune 500 companies. I know how to ship reliable systems at scale. The MSc taught me research rigour: methodology, ethics, statistical power, ecological validity.

Now I'm building the tool I needed to test whether this approach works. Not as a commercial product first, but as a research study. We'll publish findings. We'll share what works and what doesn't. We'll contribute to the evidence base instead of adding to the noise.

If closing the perception gap helps, we'll know. If the interventions work for some people but not others, we'll see the patterns. If the whole hypothesis is wrong, we'll have the data to show it.

That's the point of research: testing ideas properly, not selling solutions.