Workplace Sleep Deprivation & Fatigue Risk Survey
Assesses how much sleep loss is affecting your workforce's alertness, mood, and safety on the job — built for HR, EHS, and shift-operations teams. An AI follow-up interview digs into the real story behind a recent fatigue-related close call or bad day, surfacing root causes that a checklist alone would miss.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
On average, how many hours of sleep have you gotten per night over the past 30 days?
In the past 7 nights, on how many nights did you get less than 6 hours of sleep?
- 0 nights
- 1-2 nights
- 3-4 nights
- 5-6 nights
- 7 nights
In the past month, how often did you experience each of the following?
- Trouble falling asleep
- Waking up during the night and struggling to fall back asleep
- Waking up feeling unrested even after a full night in bed
- Using caffeine or energy drinks to stay alert during your shift
- Feeling drowsy while doing safety-sensitive tasks (driving, operating equipment, etc.)
What is the single biggest reason you don't get enough sleep on workdays?
- Work schedule or shift timing
- Long or unpredictable hours / overtime
- Stress or worry about work
- Caregiving responsibilities at home
- Screen time or trouble winding down
- A health condition (pain, apnea, etc.)
How manageable is your current work schedule for getting adequate sleep?
Thinking about days when you're running short on sleep, how much does it affect each of the following?
- Concentration and focus
- Mood or irritability
- Physical safety on the job
- Overall productivity
In the past 6 months, have you had a near-miss, error, or safety incident that you'd attribute at least partly to being tired or drowsy?
- Yes
- No
- Not sure
Overall, how well-rested do you typically feel when you start your workday?
Reconstruct one specific recent day when the respondent felt notably tired or drowsy at work: what caused the sleep loss the night before, how the fatigue showed up during the shift (mistakes, slowed reactions, mood, near-misses), and what they or their team did about it in the moment. If they reported a near-miss or safety incident tied to drowsiness, get concrete details on what happened and whether it was reported. If they say fatigue rarely affects them, probe what specifically helps them recover sleep despite a demanding schedule.
Almost done — just a couple of background questions to help us compare results across teams and shifts.
Which best describes your typical work schedule?
- Regular daytime hours
- Evening shift
- Night shift
- Rotating or on-call shift
- Prefer not to say
What is your age range?
- Under 25
- 25-34
- 35-44
- 45-54
- 55-64
- 65 or older
- Prefer not to say
What is your gender?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Thank you for sharing this with us. Your responses will be combined with your team's (never shared individually with managers) to guide scheduling changes and fatigue-risk programs.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Goes beyond a static questionnaire with an AI follow-up interview that reconstructs one specific recent day the respondent felt dangerously tired, surfacing root causes a checklist can't reach.
- Combines quantitative core questions (average sleep hours, nights under 6 hours, a matrix of fatigue symptoms, and a slider matrix on how sleep loss affects specific job tasks) with that qualitative deep-dive, so you get both prevalence data and the real story.
- Directly ties fatigue to safety outcomes with a dedicated near-miss/error/incident question, plus schedule and demographic questions at the end for benchmarking across teams and shifts.
- Built for HR/EHS/shift-ops workflows with automated per-response quality scoring and auto-generated reports, so results are usable without manual coding of open-ended answers.
Jotform
Sleep Deprivation Survey Form TemplateA fielding-ready static form template covering general sleep deprivation topics rather than a workplace-fatigue-and-safety specific instrument. It's easy to customize and embed within Jotform's broader form ecosystem, but the questions are fixed once deployed.
What it does well
- Simple drag-and-drop customization
- Large existing template library and integrations
- Straightforward embedding/distribution via Jotform's form platform
Where it falls short
- No adaptive AI follow-up interview to probe specific fatigue incidents
- No voice AI interview option
- No automated per-response quality scoring or narrative report generation
QuestionPro
Sleep Deprivation Survey + Sample Questionnaire TemplateA sample questionnaire aimed at general sleep deprivation research, presented as a fixed-question template with typical survey-platform analytics behind it. It's positioned more broadly (research/health) than as a workplace safety/EHS-specific fatigue risk tool.
What it does well
- Established survey research platform with mature analytics/reporting
- Sample questionnaire structure to start from
- Supports standard branching/logic for a fixed question set
Where it falls short
- No adaptive AI interview that digs into a specific fatigue-related incident
- No transparent prompt-level methodology disclosed
- No built-in automated quality scoring of open-text responses
SurveySparrow
Sleep Deprivation Survey TemplateA conversational-style survey template filed under healthcare, giving a chat-like feel similar to QuestionPunk's interface but with pre-set questions rather than true AI-driven follow-up. It's a ready-to-field template, useful for quick deployment but not tailored to shift-work/safety incident investigation.
What it does well
- Conversational chat-style UI that feels approachable to respondents
- Mobile-friendly template delivery
- Part of a broader healthcare template category
Where it falls short
- Fixed conversational flow, not adaptive AI probing of individual incidents
- No voice AI interview capability
- No automated report generation tying fatigue to safety/root-cause analysis
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.