Connecting Through Technology During the Coronavirus Disease 2019 Pandemic: Avoiding “Zoom Fatigue”

This is not an experimental study but a narrative review and editorial. The author synthesised emerging observations and early research (primarily from April–June 2020) on the psychological effects of videoconferencing during the COVID-19 pandemic. The "intervention" examined is videoconferencing (specifically platforms like Zoom, Skype, FaceTime) as a replacement for in-person interaction. The comparator is in-person communication or audio-only calls. The outcome measures discussed include:

Self-reported mental fatigue and exhaustion after video calls

Subjective cognitive load during video meetings

Perceived social connection and rapport

Non-verbal communication breakdowns (eye gaze, gesture, posture)

Physical discomfort (eye strain, headaches, neck pain)

The author does not report any controlled experimental data. Instead, she draws on theoretical frameworks from media psychology (e.g., media richness theory, social presence theory) and early survey data to propose mechanisms for why videoconferencing is uniquely tiring.

No original participants were studied. The review draws on:

General population observations from early pandemic (March–June 2020)

Prior research on video-mediated communication from the 1990s–2010s

Theoretical models from human-computer interaction and social psychology

Anecdotal reports from media articles and professional experience

The author does not specify sample sizes, demographics, or recruitment methods for any of the cited sources. This is a major limitation—the review is based on expert opinion and early qualitative observations, not systematic data collection.

No measurement instruments were used. The author discusses:

Self-report surveys of fatigue (no specific scales named)

Observations of meeting duration and frequency

Theoretical constructs like "cognitive load" and "social presence" (not directly measured)

Physical symptoms (eye strain, headache) reported anecdotally

There are no validated psychometric scales, physiological measures (e.g., heart rate variability, EEG), or behavioural metrics reported. The paper is entirely conceptual.

**Study design:** This is a narrative review and editorial published in a peer-reviewed journal. It is not a systematic review, meta-analysis, or original experiment. The author summarises her own observations and selectively cites prior literature to support her arguments.

**Key design features:**

No systematic search strategy (no databases, keywords, or inclusion/exclusion criteria reported)

No quality assessment of cited studies

No quantitative synthesis of effect sizes

No pre-registration or protocol

**What this design can prove:**

Generate hypotheses about why videoconferencing might cause fatigue

Identify plausible mechanisms based on established communication theory

Provide practical recommendations grounded in expert opinion

**What this design cannot prove:**

Cannot establish causality (e.g., that videoconferencing *causes* more fatigue than in-person meetings)

Cannot quantify the magnitude of the effect

Cannot rule out alternative explanations (e.g., pandemic stress, home distractions, poor ergonomics)

Cannot generalise to specific populations or contexts

Cannot compare different videoconferencing platforms or features

**Major methodological weaknesses:**

No data collection whatsoever

No control group or comparison condition

No blinding or randomisation

No statistical analysis

Publication in an editorial format means it was not peer-reviewed as a research article

The author is the journal editor, creating potential conflict of interest

The author proposes four primary mechanisms for "Zoom fatigue," each based on theoretical reasoning rather than empirical data:

1. **Excessive amounts of close-up eye contact**

- In normal conversation, people look at each other intermittently (30–60% of the time). On video calls, faces are shown at close range (often filling the screen), and the speaker sees multiple faces staring directly at them.

- This triggers a heightened state of arousal because close-up eye contact in real life signals intimacy, threat, or dominance. The brain cannot habituate to this constant gaze.

- No quantitative data provided on gaze duration or arousal levels.

2. **Constant self-view in the video mirror**

- Seeing your own face in real-time (the "mirror" window) increases self-consciousness and self-monitoring. You become a performer watching your own performance.

- This is cognitively demanding because you must simultaneously manage your facial expressions, posture, and background while processing the conversation.

- No data on how much cognitive load this adds.

3. **Reduced mobility**

- In-person meetings allow natural movement: shifting in your chair, walking to a whiteboard, gesturing freely. Video calls tether you to a fixed camera position.

- Reduced physical movement is associated with decreased cognitive performance and increased fatigue.

- No data on movement reduction or its effects.

4. **Higher cognitive load from non-verbal processing**

- Video calls degrade non-verbal cues: eye gaze is misaligned (camera vs. screen), gestures are partially visible, body language is cropped, and audio delays disrupt turn-taking.

- The brain must work harder to interpret these degraded signals, leading to faster exhaustion.

- No quantitative measure of cognitive load provided.

**Secondary observations:**

Women may experience more Zoom fatigue than men, possibly due to higher self-presentation concerns and greater caregiving responsibilities during lockdown (no data provided).

Longer meetings (>30 minutes) are more fatiguing than shorter ones (no threshold data).

Back-to-back video calls are worse than spaced calls (no recovery time data).

No effect sizes are reported because no data were collected. The author does not quantify:

How much more fatiguing video calls are compared to audio calls

How much fatigue increases per hour of videoconferencing

What percentage of people experience clinically significant fatigue

How long fatigue persists after a video call ends

The paper is entirely qualitative. The only "magnitude" is the author's assertion that "many" people report fatigue and that it is "significant" enough to warrant attention.

**What the authors acknowledge:**

The paper is an editorial, not a research study

The observations are preliminary and based on early pandemic conditions

More research is needed to confirm the proposed mechanisms

**What a critical reader would note:**

1. **No empirical data:** The paper contains zero original data, no surveys, no experiments, no physiological measures. It is expert opinion.

2. **No systematic review methodology:** The author does not describe how she identified, selected, or synthesised the literature. This introduces selection bias—she may have only cited studies that support her argument.

3. **Confounding variables:** The pandemic context is a massive confound. People were experiencing stress, isolation, homeschooling, job insecurity, and health anxiety simultaneously. Any fatigue attributed to Zoom could be caused by these factors.

4. **No comparison condition:** Without comparing video calls to in-person meetings, audio calls, or other remote work modalities, we cannot isolate the effect of video.

5. **Publication bias:** The paper was published in the author's own journal, and she is the editor-in-chief. This creates a conflict of interest and bypasses normal peer review.

6. **No dose-response data:** The author does not specify how many hours of video calls cause fatigue, or whether there is a threshold effect.

7. **Individual differences ignored:** Some people may thrive on video calls (e.g., extroverts, people with social anxiety who prefer mediated interaction). The paper treats all users as identical.

8. **Platform specificity:** The paper focuses on Zoom but the mechanisms would apply to any videoconferencing platform. No comparison between platforms is made.

9. **No long-term data:** The paper was written in June 2020, only 3–4 months into the pandemic. Long-term adaptation to video calls is unknown.

10. **No practical solutions tested:** The author suggests turning off self-view and taking audio breaks, but provides no evidence that these interventions reduce fatigue.

For someone running their own n=1 experiment to test whether videoconferencing causes fatigue and what mitigates it:

### What to test (specific intervention and dose)

**Primary intervention:** Compare a day with 4 hours of video calls (e.g., Zoom, Google Meet) vs. a day with 4 hours of audio-only calls (phone or voice-only mode).

**Secondary intervention:** On video call days, test turning off your self-view (hide the mirror window) for half the calls vs. leaving it on.

**Tertiary intervention:** Test taking a 5-minute break every 30 minutes of video vs. no breaks.

### Minimum meaningful duration

Run each condition for at least 3–5 days to account for daily variation in workload, sleep, and stress.

A full 2-week block per condition (14 days video, 14 days audio) would give more reliable data.

Avoid testing on Mondays or Fridays (different work demands) or during high-stress periods.

### What to measure (specific metrics)

**Primary outcome:** Subjective fatigue on a 0–10 scale (0 = fully alert, 10 = exhausted) measured immediately after each call and at the end of each workday.

**Secondary outcomes:**

- Cognitive performance: Use a simple reaction time test (e.g., the "Digit Symbol Substitution Test" or a free online Stroop test) before and after each call block.

- Physical symptoms: Rate eye strain, headache, and neck pain on 0–10 scales.

- Social connection: Rate "how connected I felt to the other person" on a 0–10 scale after each call.

- Meeting productivity: Rate "how much I accomplished" on a 0–10 scale.

**Objective measure:** Track total call duration and number of calls per day using your calendar or a time tracker app.

### Key confounds to control for

**Call content:** Keep the type of calls similar across conditions (e.g., all one-on-one meetings, all group meetings, or mix consistently).

**Time of day:** Schedule calls at the same times each day to control for circadian fatigue.

**Sleep:** Track sleep duration and quality (e.g., using a sleep diary or wearable). Poor sleep will increase fatigue regardless of call type.

**Caffeine and alcohol:** Log consumption. Both affect fatigue and cognitive performance.

**Physical activity:** Log exercise. Sedentary days increase fatigue.

**Stress:** Rate daily stress on a 0–10 scale. High-stress days will confound results.

**Screen time outside calls:** Track total screen time (email, social media, streaming) to control for digital fatigue from other sources.

**Ergonomics:** Keep your chair, desk, lighting, and monitor position identical across conditions.

**Social context:** Note whether calls are with colleagues, friends, or family—different relationships may produce different fatigue levels.

### What a positive result would look like

**Video vs. audio:** You report fatigue scores that are consistently 2+ points higher (on the 0–10 scale) on video call days compared to audio-only days, with at least 80% of days showing this pattern.

**Self-view on vs. off:** Fatigue scores are 1–2 points lower on days when you hide your self-view, and you feel less self-conscious (qualitative notes).

**Breaks vs. no breaks:** Fatigue scores are 1–2 points lower on days with 5-minute breaks every 30 minutes, and your cognitive performance (reaction time) does not decline across the day.

**Combined effect:** The largest reduction in fatigue occurs when you combine audio-only calls + self-view off + breaks. This would suggest that all three mechanisms contribute to Zoom fatigue.

**Important caveat:** This is an n=1 experiment. You cannot generalise your results to others. But you can identify what works for *you*. If you find that audio-only calls reduce your fatigue by 3 points, that is a meaningful personal improvement worth adopting as a habit.