Calculator Inputs
Example Data Table
Sample weekly snapshot showing how inputs can look. Replace with your own screen-time values.
| Day | Online Minutes | Late-night Minutes | Sessions | Notifications/hr | Breaks | Sleep (hrs) |
|---|---|---|---|---|---|---|
| Mon | 195 | 20 | 18 | 8 | 5 | 7.2 |
| Tue | 210 | 30 | 21 | 10 | 4 | 6.8 |
| Wed | 165 | 10 | 16 | 7 | 6 | 7.6 |
| Thu | 225 | 35 | 23 | 12 | 4 | 6.5 |
| Fri | 180 | 25 | 17 | 9 | 5 | 7.0 |
| Sat | 260 | 40 | 26 | 14 | 3 | 6.2 |
| Sun | 205 | 15 | 19 | 8 | 5 | 7.4 |
Formula Used
The calculator converts each behavior into a normalized factor, then applies weights to form one index (0–100).
- Time compares daily minutes to your chosen limit.
- Late compares late-night minutes to your late-night limit.
- Notif compares alerts per hour to your notification limit.
- Sessions estimates attention fragmentation from pickups.
- Breaks reflects how close you are to your break target.
- Mood/Anxiety/Sleep add self-reported strain signals.
- 0–24: Low strain, keep habits steady.
- 25–49: Moderate strain, tighten one boundary.
- 50–74: High strain, reduce drivers and add breaks.
- 75–100: Severe strain, simplify use and seek support if needed.
How to Use This Calculator
- Open your device’s screen-time dashboard and note weekly averages.
- Enter average daily minutes, late-night minutes, sessions, and notifications.
- Add breaks, sleep, mood, and anxiety to capture context.
- Set your personal limits if you already have targets.
- Press Submit to see results above the form.
- Use the suggested cap and break rhythm for seven days, then reassess.
Behavior baselines and weekly averaging
Weekly averages smooth daily spikes and reveal real patterns. Track total online minutes, sessions, and late-night minutes for at least seven days. A 30–60 minute rise in late-night time often predicts next-day fatigue and more unplanned sessions. Use device reports to capture both phone and desktop activity for completeness. Document your baseline limits so future comparisons are consistent. Consider separating work platforms from entertainment apps in your notes.
Attention fragmentation metrics
Sessions per day highlights how often attention is interrupted. Many short sessions can feel more draining than fewer longer blocks, even at similar totals. If sessions exceed your target by 20%, reduce app switching by grouping tasks into two or three focused windows. Aim for a stable average session length rather than chasing zero pickups. Small reductions are meaningful when they persist across weeks.
Notification load and stress signals
Notifications per hour is a strong lever because it drives reactive checking. When alerts exceed your limit, the index rises even if total minutes stay constant. Silencing nonessential categories and using scheduled summaries can lower interruption rates quickly. Watch anxiety and mood ratings alongside notifications to spot cause-and-effect.
Break compliance and recovery
Break compliance compares breaks taken to your break target. A compliance rate under 60% often correlates with higher strain scores and shorter sleep. Try a simple rhythm: 55 minutes online, then 5 minutes offline, or 45/10 during heavy workloads. Micro-breaks count when they are device-free and include movement or distance viewing. Pair reduced late-night use with a consistent wind-down routine.
Interpreting the Online Load Index
The Online Load Index combines time, late-night use, notifications, sessions, breaks, sleep, mood, and anxiety into a 0–100 score. Low and moderate bands suggest small boundary tweaks, while high and severe bands recommend reducing key drivers first. Use the suggested daily cap as a temporary target, then re-evaluate weekly to keep changes realistic. Use the export files to share summaries with a coach or clinician. For best accuracy, update inputs from the same tracking source, avoid rounding too aggressively, and review trends by weekday. When the index drops, reinforce the habit that caused it, and keep brief notes.
FAQs
What does the Online Load Index represent?
It summarizes multiple inputs into one 0–100 score showing digital strain tendency. Higher scores reflect more over-limit time, late-night use, interruptions, low breaks, and stronger self-reported strain signals.
Which inputs matter most for lowering my score?
Focus on the largest driver shown in your results. Reducing late-night minutes, cutting notifications, and improving break compliance typically produces the fastest improvement without needing extreme reductions in total online time.
How should I estimate sessions per day?
Use screen-time pickup counts, app opens, or your best weekly average. If you are unsure, start with a rough estimate and refine next week using the same tracking method for consistency.
Does a high score mean I have a mental health disorder?
No. The calculator is informational and cannot diagnose. A high score suggests habits that may increase stress or sleep disruption. If distress is persistent, consider speaking with a qualified professional.
Why include mood, anxiety, and sleep?
They add context that usage metrics alone can miss. Two people can have similar screen time but different well-being. These inputs help the index reflect your lived experience and guide healthier, realistic adjustments.
How often should I rerun the calculator?
Weekly is ideal. Update values from the same reporting period, compare changes, and adjust one habit at a time. Small weekly improvements compound and are easier to maintain than sudden large restrictions.