Developing the DHE Scale

Initial findings from the pilot

Progress to date

  • Scoping review of mechanisms
  • Literature review of existing scales
  • Semi-structured interviews (N=9)
  • Model development
  • Delphi study with subject-matter experts
  • Pretesing/Cognitive interviews (N=10)

Digital Health Enablement (DHE)

Survey pilot

  • 65 participants (50 Prolific, 15 paper)
  • Demographic quotas on Prolific
  • 45 items across 7 sub-constructs

Prolific completion times

Demographics: Age

Demographics: Education

Education Level Count %
No Qualifications 6 12
School-level 14 28
Vocational and technical 7 14
Further education 6 12
Higher education 1 2
Undergraduate Degree 10 20
Postgraduate and professional 6 12

Demographics: Income

Income Level Count Percentage
Less than £19,000 a year 16 32
£19,001 to £28,000 13 26
£28,001 to £38,000 10 20
£38,000 to £55,000 4 8
More than £55,000 a year 7 14

Demographics: Gender & Ethnicity

Gender Count %
Male 27 54
Female 23 46
Ethnic Group Count %
English/Welsh/Scottish/Northern Irish/British 44 88
Indian 2 4
Any other White Background 1 2
Chinese 1 2
White and Black African 1 2
White and Black Caribbean 1 2

Issues: Skew (1)

Item 18 - “I would expect health technologies to have measures in place to keep my information safe”

Issues: Skew (2)

  • 11 (of 45) items demonstrated problematic skew, ceiling/floor effects or low variance
  • This limits the ability to discriminate between individuals
  • Consider item removal or revisions
  • E.g. “I often struggle to use digital technology” could be revised to “I sometimes struggle to use digital technology”

Issues: Neutral responses (1)

Issues: Neutral responses (2)

Issues: Neutral responses (3)

Issues: Neutral responses (4)

Metric Value
Participants who used neutral ≥50% of the time 0 out of 66 (0%)
Participants who used neutral ≥30% of the time 2 out of 66 (3%)
Mean participant neutral usage 12%
Median participant neutral usage 8.9%

Issues: Method effects

  • Use of fitness app (rather than generic ‘health technology’) may create a method effect.
  • Method effects occur when similar wording across items inflates correlations.
  • Especially the case where one sub-construct relies on ‘fitness app’ exclusively
  • Solution: replace fitness app with ‘health technology’ throughout

Issues: Poor factor loadings

Factor loadings

  • Little consensus on factor loadings, but >0.7 generally considered good.
  • However, may retain items with moderate loading + good content validity. E.g. “I can usually adjust the settings on digital technology to suit my needs” (loading: 0.578)
  • Remove items with poor factor loading + poor content validity (e.g. digital health trust)
  • Solution: consider revising factors/ model.

Options

  1. Retain 7 factor model, revise problematic items.
  2. Adopt a 2 factor model based on pliot data
  3. Test 3 factor model at pilot, extend Factor 3: context and support.

3-Factor model (1)

3-Factor model (2)

1. Digital Health Motivation & Acceptance

  • Question: “Do I want to use digital health technologies?”
  • Attitudes, trust, outcome expectancy, intention

2. Technical Skills & Self-Efficacy

  • Question: “Can I technically perform the required tasks?”
  • Skills, troubleshooting confidence, technical competence

3. Digital Health Context & Support

  • Question: “Is my environment conducive to success?”
  • Social support, environmental barriers, external pressures

3-Factor Model (3)

Additional social context & support items

  • “Those close to me would support my use of a health app”
  • “Using health apps is normal in my social groups”
  • “Others like me already use health apps”
  • “Those close to me would encourage me to use a health app”
  • “Most people I know use health apps”
  • “Those close to me would help me getting going with a health app”
  • “Those close to me would encourage me to keep going with a health app”

3-Factor Model (4)

Other proposed changes

  • Change all items to health technology from fitness apps (OR VICE VERSA)
  • Revise or remove items with problematic skew.
  • Remove items with weak loadings.
  • Remove items related to Trust.
  • Remove theoretically inconsistent items from revised factors.
  • Remove Item 16 (45% neutral response).

3-Factor Model (5)

Factor Items Status Key Issues Target
Factor 1 Motivation & Acceptance 23 ⚠️ Too many Redundancy, weak items 8-10
Factor 3 Technical Skills 13 ✅ Good foundation Minor tweaks needed 6-8
Factor 2 Context & Support 8 ⚠️ Needs strengthening Few strong items, poor wording 8-10
Total 44 22-28

Factor 1: Motivation & Acceptance

  • I would use [a fitness app] because I am motivated to improve my health.
  • I would enjoy using [a fitness app] to improve my health.
  • I believe that using [a fitness app] would be good for me.
  • I can’t see why I should bother using [a fitness app].
  • Because I am not interested in using technology to support my health, I wouldn’t use [a fitness app].
  • I believe that using [a fitness app] would improve my physical health.
  • I don’t think [a fitness app] would work for me.
  • I would get a sense of accomplishment if I used [a fitness app].
  • I would use [a fitness app] because I want to learn about my health.
  • I believe that using [a fitness app] would lead to me living a healthier lifestyle.
  • I would use [a fitness app], because I find exploring new health technologies exciting.

Factor 2: Technical Skills & Self-Efficacy

  • I can troubleshoot basic issues with digital technology.
  • I can usually use digital technology.
  • I am confident I could use the advanced features of [a fitness app] if I wanted to.
  • I would understand what [a health technology] was asking me to do to improve my health.
  • I often struggle to use digital technology.
  • I am confident I could troubleshoot basic issues with [a fitness app] if I wanted to.
  • I am confident I could use [a fitness app] without help if I wanted to.
  • I am confident I could learn how to use [a fitness app] if I wanted to.
  • I can usually adjust the settings on digital technology to suit my needs.

Factor 3: Digital Health Context & Support

  • I would use [a fitness app] because I would feel guilty if I didn’t.
  • I would use [a fitness app] because I don’t want to let others down.
  • I don’t have anyone in my life that could help me use [a health technology].
  • Few people I know use [health technologies].
  • Those close to me would support my use of [a health app].
  • Using [health apps] is normal in my social groups.
  • Others like me already use [health apps].
  • Those close to me would encourage me to use a [health app].
  • Most people I know use [health apps].
  • Those close to me would help me getting going with a [health app].
  • Those close to me would encourage me to keep going with a [health app].

Next steps

  1. Agree revisions/new model
  2. Conduct new pilot with revised items/models
  3. Test existing validated digital technology scales alongside DHE, e.g. Digital Health Literacy Instrument (DHLI)