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Healthcare & Clinical

Supplement & Wearable Stack Optimization Survey

Understand how self-directed health consumers choose, combine, and abandon supplements and wearables — what they spend, which data or advice actually changes their behavior, and where an AI follow-up interview surfaces the real story behind their most recent stack change instead of the tidy version.

Sample questions

A preview of what’s in the template. Every question is editable before you launch.

14 questions · ~7 min
Q01
Message

Hi! We're studying how people build and manage their own supplement and wearable routines — what you take, what you track, and why you keep or ditch things. Your responses are completely confidential and anonymized. Honest answers help most. About 6-7 minutes.

Q02
Multiple ChoiceRequired

Which wearables or health-tracking devices do you currently use regularly (at least a few times a week)?

  • Fitness/activity tracker (e.g., wrist band)
  • Smartwatch with health features
  • Continuous glucose monitor
  • Sleep tracker (ring, mat, or app-connected)
  • Heart rate variability / recovery device
  • Smart scale or body composition device
Q03
Multiple ChoiceRequired

Which of these supplement categories are you currently taking?

  • Multivitamin or general micronutrient
  • Protein or amino acids
  • Sleep support (e.g., magnesium, melatonin)
  • Cognitive/nootropic support
  • Gut health or probiotics
  • Joint, hormone, or longevity-focused compounds
  • Energy or pre-workout support
Q04
Point AllocationRequired

Roughly how does your typical monthly health-optimization budget split across these categories? Please make the numbers add up to 100.

  • Supplements
  • Wearable devices or subscriptions
  • Lab testing or biomarker panels
  • Coaching or expert guidance
  • Apps or tracking software
Allocate 100 points
Q05
MatrixRequired

How much do you agree with each statement about how you manage your own stack?

4 rows × 5 columns
  • I check my wearable data before deciding whether to keep taking a supplement
  • I adjust dosage or timing based on how I feel more than on any data
  • I trust my own tracked data more than a doctor's general advice
  • I stop taking something quickly if I don't see a measurable change
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q06
Best–Worst Trade-off (MaxDiff)Required

When deciding whether to add a new supplement or wearable to your routine, which factors matter most versus least?

  • Published research or clinical studies
  • Correlation with my own wearable or biomarker data
  • Recommendation from a doctor or clinician
  • Recommendation from an influencer, podcast, or forum
  • Price and ongoing cost
  • Brand or company reputation
  • My own trial-and-error results
  • Risk of side effects
Pick best & worst per setBest:Matters mostWorst:Matters least
Q07
Opinion ScaleRequired

How confident are you that your current supplement and wearable stack is actually improving your health outcomes?

Scale: 17
Min:Not confident at allMax:Extremely confident
Q08
Multiple ChoiceRequired

In the last 6 months, what most often made you stop taking a supplement or using a device?

  • No felt improvement
  • Wearable or lab data didn't shift
  • Too expensive to keep up
  • Side effects or discomfort
  • A doctor advised against it
  • Just ran out and didn't reorder
Q09
AI Interview

Walk the respondent through the most recent time they added or dropped a specific supplement or wearable feature. Get them to name the exact product/feature, the specific data point or feeling that triggered the decision, how long they gave it before judging, and whether they'd reverse the decision if new evidence appeared. If they said they trust their own data over a doctor's, probe a concrete example where that played out.

Q10
Long Text

What's the single biggest frustration or gap in trying to figure out what's actually working in your stack?

Q11
Message

Just a couple of quick background questions, then you're done.

Q12
Multiple Choice

Which age range do you fall into?

  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65+
  • Prefer not to say
Q13
Multiple Choice

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q14
Message

Thank you for the detailed answers! We'll use these to understand how self-directed health consumers actually make and unmake decisions about supplements and wearables, and to spot where better data or guidance could help.

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 static rating questions with an AI follow-up interview that walks the respondent through the most recent specific time they added or dropped a supplement or wearable, surfacing the real story instead of a tidy summary
  • Uses a constant-sum budget-split question and a max-diff exercise to get relative, trade-off-based data on spending and decision drivers rather than just agree/disagree ratings
  • Combines quantitative structure (multiple-choice device/category use, matrix agreement statements, confidence scale) with a long-text gap/frustration question and adaptive probing on top
  • Every prompt in the flow is transparent and the resulting responses roll into an auto-generated report, so teams can see exactly what was asked and why a given follow-up fired

Jotform

Supplemental Health Questionnaire Form Template

A ready-to-use static form template for collecting supplement-related health information, built on Jotform's drag-and-drop form builder. It's oriented toward straightforward data capture (e.g., current supplement use) rather than exploring purchase decisions, budget trade-offs, or abandonment behavior in depth. No mention of adaptive interviewing or behavioral scoring — it's a fielding-ready form, not a research instrument.

What it does well

  • Quick to deploy and customize using Jotform's drag-and-drop builder
  • Purpose-built around supplement-related health questions, so it's topically on-point
  • Likely integrates with Jotform's broader form ecosystem (notifications, integrations, PDF exports)

Where it falls short

  • Static question set with no adaptive AI follow-up to probe the story behind a specific behavior change
  • No automated per-response quality scoring or transparent prompt methodology
  • No structured way to capture relative trade-offs (e.g., budget splits or max-diff decision drivers) beyond standard field types

Typeform

Health Self-Assessment Quiz Form Template

A general health self-assessment quiz template with Typeform's conversational, one-question-at-a-time interface. It's framed as a personal self-assessment quiz rather than a research survey into supplement/wearable purchasing, stacking, or abandonment behavior, so most of QuestionPunk's target constructs (budget allocation, decision trade-offs, recent behavior change) aren't its focus. Good UX polish, but still a fixed-question flow.

What it does well

  • Polished, conversational one-question-at-a-time interface that's pleasant for respondents
  • Easy to customize branching logic within Typeform's builder
  • Broad applicability as a general health self-assessment starting point

Where it falls short

  • No adaptive AI interview or voice interview capability to dig into a specific recent behavior change
  • Not purpose-built for supplement/wearable stack research (budget split, category trade-offs, abandonment triggers)
  • No automated quality scoring or transparent, publishable prompt methodology

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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