Data Summary

This report presents preliminary analyses of a digital health readiness scale developed to assess individuals’ preparedness to engage with digital health technologies. The scale comprises 45 items across seven theoretical sub-constructs: Digital Health Literacy, Digital Skills, Sociodigital Influences, Digital Trust, Digital Self-Efficacy, Digital Behavioural Regulation, and Digital Outcome Expectancy.

The analyses below examine item-level performance, including mean responses, internal consistency, and factor structure based on pilot data from 50 participants. Given the small sample size, these results should be interpreted cautiously and are intended to identify potential measurement issues and guide scale refinement prior to full scale validation.

Note: This is pilot-stage data analysis. Additional participants who completed paper versions of the survey are yet to be included, which may alter the patterns observed here.

Item score means

Visualising item score means is useful for the following reasons:

1. Ceiling/floor effects:

Items with a mean around 1-2 suggests strong disagreement - might indicate items that don’t resonate or are poorly worded. Items with a mean near 6-7 suggest strong agreement or socially desirable responding. Items with a mean around 4 suggest neutral responses which might indicate ambiguous wording (“I’m unsure how to answer this”) or genuine ambivalence.

For example, few people are likely to disagree with the statement:

‘I would expect health technologies to have measures in place to keep my information safe’,

…limiting its ability to discriminate between individuals.

2. Response patterns across subconstructs:

Visualising means helps to identify broad response patterns across different sub constructs. For example, Digital Skills items (6-10) may show higher means if respondents feel confident with basic technology whereas Trust items (17-21) might cluster around the midpoint if respondents have mixed feelings about data security.

In general, we would expect means and responses distributions to vary between constructs measuring different things.

3. Problematic items to investigate:

Things to look out for:

Extreme outliers (much higher/lower than similar items) - check wording
Items well below midpoint - might be reverse-coded or poorly understood
Attention checks should show expected patterns (Items 22-23, 48-49)
Are negatively worded items (like Item 9 “I often struggle…”) showing appropriately reversed patterns?

Distribution of item means by sub-construct

The following chart shows the distribution of means across different sub-constructs, prior to reverse coding.

Self-efficacy

Items in the self-efficacy sub-construct all share a high mean score, clustering around 6. This suggests respondents have a high degree of confidence in their own confidence in using adopting a health technology.

This is perhaps surprising given the mean scores for (current) digital score are lower. Further, items that might be expected to score lower, such as:

I am confident I could use the advanced features of a fitness app if I wanted to.

…also scored a relatively high mean (M = 5.82), albeit there was a small difference between online (M = 5.92) and offline (M = 5.47).

Correlation Analysis

How correlated should scale items be?

Devellis et al. (2022) suggest the following thresholds:

<.60, unacceptable
.60-.65, undesirable
.65 and .70, minimally acceptable
.70 and .80, respectable
.80 and .90,, very good
>.90, consider shortening the scale

Item Analysis: Correlations
	Item Text	Sub-Construct	Mean	r with Sub-domain	r with Total Scale
Item_1	I would understand what a health technology was asking me to do to improve my health.	Digital Health Literacy	6.03	0.200	0.484
Item_2	I can usually work out if health information online is reliable.	Digital Health Literacy	5.11	0.253	-0.068
Item_3	I usually know when health information online is wrong.	Digital Health Literacy	4.75	0.186	0.044
Item_4	I would understand information about my health from health technologies.	Digital Health Literacy	5.75	0.288	0.519
Item_5	I would know which health technology was right for me.	Digital Health Literacy	4.95	0.221	0.473
Item_6	I can usually download apps.	Digital Skills	6.58	0.502	0.564
Item_7	I can usually use digital technology.	Digital Skills	6.11	0.611	0.392
Item_8	I can troubleshoot basic issues with digital technology.	Digital Skills	5.72	0.603	0.409
Item_9	I often struggle to use digital technology.	Digital Skills	5.66	0.465	0.401
Item_10	I can usually adjust the settings on digital technology to suit my needs.	Digital Skills	5.72	0.500	0.541
Item_11	Those close to me would encourage me to use a new health technology.	Sociodigital Influences	4.40	0.314	0.654
Item_12	Those close to me would help me use a new health technology if needed.	Sociodigital Influences	5.26	0.342	0.455
Item_13	I don't have anyone in my life that could help me use a health technology.	Sociodigital Influences	5.48	0.222	0.320
Item_14	I feel like using health technology is expected these days.	Sociodigital Influences	4.35	0.175	0.433
Item_15	Few people I know use health technologies.	Sociodigital Influences	4.18	0.053	0.045
Item_16	Others would be pleased I was using a health technology.	Sociodigital Influences	4.43	0.266	0.344
Item_17	I would trust a new health technology with my personal details.	Digital Trust	4.42	0.591	0.555
Item_18	I would expect health technologies to have measures in place to keep my information safe.	Digital Trust	5.92	0.409	0.513
Item_19	I would trust a new health technology to protect my personal information.	Digital Trust	4.55	0.591	0.629
Item_20	I worry that using a new health technology would put me at risk of fraud.	Digital Trust	5.32	0.405	0.350
Item_21	I would trust a new health technology with information about my health conditions.	Digital Trust	4.89	0.482	0.540
Item_22	Select Strongly Disagree for this question.	Attention Check	1.00	NA	NA
Item_23	Select Strongly Agree for this question.	Attention Check	7.00	NA	NA
Item_24	I am confident I could learn how to use a fitness app if I wanted to.	Digital Self-Efficacy	6.32	0.484	0.605
Item_25	I am confident I could use a fitness app to monitor my health if I wanted to.	Digital Self-Efficacy	6.31	0.483	0.611
Item_26	I am confident I could use a fitness app regularly if I wanted to.	Digital Self-Efficacy	5.85	0.448	0.694
Item_27	I am confident I could use a fitness app without help if I wanted to.	Digital Self-Efficacy	6.09	0.496	0.511
Item_28	I am confident I could troubleshoot basic issues with a fitness app if I wanted to.	Digital Self-Efficacy	5.58	0.400	0.444
Item_29	I am confident I could use the advanced features of a fitness app if I wanted to.	Digital Self-Efficacy	5.82	0.532	0.451
Item_30	I would use a fitness app because I want to learn about my health.	Digital Behavioural Regulation	5.35	0.541	0.782
Item_31	Because I am not interested in using technology to support my health, I wouldn't use a fitness app.	Digital Behavioural Regulation	5.58	0.561	0.853
Item_32	I wouldn't use a a fitness app because I find technology frustrating.	Digital Behavioural Regulation	6.00	0.361	0.633
Item_33	I would use a fitness app because I am motivated to improve my health.	Digital Behavioural Regulation	5.46	0.575	0.819
Item_34	I would use a fitness app because I don't want to let others down.	Digital Behavioural Regulation	5.55	-0.055	-0.143
Item_35	I would enjoy using a fitness app to improve my health.	Digital Behavioural Regulation	5.20	0.546	0.784
Item_36	I would use a fitness app because using technology is part of who I am.	Digital Behavioural Regulation	4.31	0.431	0.666
Item_37	I would use a fitness app because I would feel guilty if I didn't.	Digital Behavioural Regulation	5.63	0.028	-0.007
Item_38	I can't see why I should bother using a fitness app.	Digital Behavioural Regulation	5.26	0.554	0.816
Item_39	I would use a fitness app if told to by health professionals.	Digital Behavioural Regulation	5.54	0.311	0.552
Item_40	I would use a fitness app, because I find exploring new health technologies exciting.	Digital Behavioural Regulation	4.60	0.516	0.739
Item_41	I would get a sense of accomplishment if I used a fitness app.	Digital Behavioural Regulation	5.14	0.443	0.736
Item_42	I believe that using a fitness app would improve my physical health.	Digital Outcome Expectancy	5.31	0.749	0.748
Item_43	I don't think a fitness app would work for me.	Digital Outcome Expectancy	5.43	0.697	0.839
Item_44	I believe that using a fitness app would be good for me.	Digital Outcome Expectancy	5.43	0.757	0.836
Item_45	I believe that using a fitness app would lead to me living a healthier lifestyle.	Digital Outcome Expectancy	5.32	0.708	0.674
Item_46	I believe that using a fitness app would be good for my mental health.	Digital Outcome Expectancy	4.91	0.716	0.723
Item_47	I believe I would see improvements to my quality of life if I used a fitness app.	Digital Outcome Expectancy	4.97	0.673	0.773
Item_48	Select Strongly Agree for this question.	Attention Check	6.91	NA	NA
Item_49	Select Strongly Disagree for this question.	Attention Check	1.00	NA	NA

Factor Analysis

🔍 Factor Analysis Adequacy Tests

Kaiser-Meyer-Olkin (KMO) Test:
- Measures sampling adequacy
- whether your data is suitable for factor analysis
- Tests if variables share enough common variance to justify factor extraction
- Excellent: > 0.90 | Good: 0.80 - 0.90 | Mediocre: 0.70 - 0.80 | Poor: 0.60 - 0.70 | Unacceptable: < 0.60
- Values close to 1.0 indicate variables have strong relationships with other variables

Bartlett’s Test of Sphericity:
- Tests whether your correlation matrix is significantly different from an identity matrix
- Null hypothesis: Variables are uncorrelated (correlation matrix = identity matrix)
- Want: Significant result (p < 0.05) to proceed with factor analysis
- Non-significant result suggests variables don’t correlate enough for meaningful factor extraction

💡 Interpretation for Your Data:
- Both tests must “pass” before conducting factor analysis
- With small samples (N=65), these tests may be less reliable
- KMO values may be inflated due to limited observations per variable
- Consider these as preliminary indicators rather than definitive assessments

Factor Analysis Adequacy Tests:

Kaiser-Meyer-Olkin (KMO) Overall MSA: 0.746

Bartlett's Test of Sphericity p-value: 2.259735e-166

Scree plot

Parallel Analysis Scree Plot

A parallel analysis scree plot is a visualization technique used in factor analysis that compares the eigenvalues from your actual digital enablement survey data against eigenvalues generated from random data with the same dimensions. You should look for the point where your real data’s eigenvalues drop below the line representing random data eigenvalues - only factors above this threshold should be retained, as they explain more variance than would be expected by chance alone.

Parallel analysis suggests that the number of factors =  3  and the number of components =  NA

Parallel Analysis suggests 3 factors

Factor Analysis using method =  ml
Call: fa(r = fa_data, nfactors = n_factors_pa, rotate = "oblimin", 
    scores = TRUE, fm = "ml")
Standardized loadings (pattern matrix) based upon correlation matrix
        item   ML1   ML2   ML3    h2   u2 com
Item_33   31  0.91             0.851 0.15 1.0
Item_35   33  0.89             0.788 0.21 1.0
Item_44   42  0.85             0.882 0.12 1.2
Item_38   36  0.84             0.749 0.25 1.1
Item_31   29  0.83             0.819 0.18 1.1
Item_11   11  0.83             0.686 0.31 1.2
Item_42   40  0.83             0.773 0.23 1.3
Item_43   41  0.82             0.818 0.18 1.2
Item_46   44  0.81             0.713 0.29 1.1
Item_41   39  0.80             0.700 0.30 1.1
Item_30   28  0.80             0.732 0.27 1.2
Item_47   45  0.78             0.662 0.34 1.1
Item_45   43  0.74             0.659 0.34 1.4
Item_40   38  0.74             0.614 0.39 1.1
Item_21   21  0.70             0.437 0.56 1.1
Item_39   37  0.64             0.400 0.60 1.2
Item_16   16  0.64             0.391 0.61 1.6
Item_19   19  0.64             0.449 0.55 1.3
Item_17   17  0.63             0.438 0.56 1.6
Item_36   34  0.56             0.481 0.52 1.4
Item_14   14  0.47             0.226 0.77 1.1
Item_18   18  0.39             0.227 0.77 1.3
Item_12   12  0.39             0.233 0.77 1.5
Item_20   20                   0.125 0.88 2.1
Item_8     8        0.93       0.780 0.22 1.0
Item_7     7        0.83       0.649 0.35 1.1
Item_29   27        0.77       0.628 0.37 1.0
Item_1     1        0.64       0.479 0.52 1.1
Item_9     9        0.64       0.418 0.58 1.1
Item_28   26        0.62       0.446 0.55 1.3
Item_27   25        0.62       0.483 0.52 1.2
Item_24   22        0.59       0.523 0.48 1.3
Item_10   10        0.58  0.51 0.716 0.28 2.0
Item_6     6        0.58       0.506 0.49 1.4
Item_25   23  0.34  0.45       0.450 0.55 1.9
Item_2     2        0.31       0.115 0.88 2.0
Item_3     3        0.30       0.092 0.91 1.5
Item_4     4              0.57 0.510 0.49 1.5
Item_37   35              0.55 0.297 0.70 1.2
Item_32   30  0.30  0.38  0.51 0.677 0.32 2.5
Item_26   24  0.37  0.34  0.50 0.705 0.30 2.7
Item_34   32              0.47 0.269 0.73 1.7
Item_13   13              0.36 0.198 0.80 1.8
Item_5     5              0.33 0.295 0.71 2.6
Item_15   15                   0.047 0.95 1.2

                        ML1  ML2  ML3
SS loadings           13.84 6.32 2.97
Proportion Var         0.31 0.14 0.07
Cumulative Var         0.31 0.45 0.51
Proportion Explained   0.60 0.27 0.13
Cumulative Proportion  0.60 0.87 1.00

 With factor correlations of 
     ML1  ML2  ML3
ML1 1.00 0.35 0.17
ML2 0.35 1.00 0.15
ML3 0.17 0.15 1.00

Mean item complexity =  1.4
Test of the hypothesis that 3 factors are sufficient.

df null model =  990  with the objective function =  57.26 with Chi Square =  2757.89
df of  the model are 858  and the objective function was  27.82 

The root mean square of the residuals (RMSR) is  0.08 
The df corrected root mean square of the residuals is  0.09 

The harmonic n.obs is  65 with the empirical chi square  806.6  with prob <  0.89 
The total n.obs was  65  with Likelihood Chi Square =  1284.24  with prob <  1.5e-19 

Tucker Lewis Index of factoring reliability =  0.703
RMSEA index =  0.086  and the 90 % confidence intervals are  0.078 0.098
BIC =  -2297.38
Fit based upon off diagonal values = 0.96
Measures of factor score adequacy             
                                                   ML1  ML2  ML3
Correlation of (regression) scores with factors   0.99 0.97 0.94
Multiple R square of scores with factors          0.98 0.94 0.87
Minimum correlation of possible factor scores     0.96 0.88 0.75

Factor Analysis Results: Loadings and Communalities (loadings < 0.30 suppressed)
Item	Item_Text	Theoretical_Construct	ML1	ML2	ML3	Communality	Uniqueness
Item_33	I would use a fitness app because I am motivated to improve my health.	Digital Behavioural Regulation	0.914			0.851	0.149
Item_35	I would enjoy using a fitness app to improve my health.	Digital Behavioural Regulation	0.889			0.788	0.212
Item_44	I believe that using a fitness app would be good for me.	Digital Outcome Expectancy	0.849			0.882	0.118
Item_38	I can't see why I should bother using a fitness app.	Digital Behavioural Regulation	0.840			0.749	0.251
Item_31	Because I am not interested in using technology to support my health, I wouldn't use a fitness app.	Digital Behavioural Regulation	0.834			0.819	0.181
Item_11	Those close to me would encourage me to use a new health technology.	Sociodigital Influences	0.833			0.686	0.314
Item_42	I believe that using a fitness app would improve my physical health.	Digital Outcome Expectancy	0.826			0.773	0.227
Item_43	I don't think a fitness app would work for me.	Digital Outcome Expectancy	0.819			0.818	0.182
Item_46	I believe that using a fitness app would be good for my mental health.	Digital Outcome Expectancy	0.814			0.713	0.287
Item_41	I would get a sense of accomplishment if I used a fitness app.	Digital Behavioural Regulation	0.804			0.700	0.300
Item_30	I would use a fitness app because I want to learn about my health.	Digital Behavioural Regulation	0.802			0.732	0.268
Item_47	I believe I would see improvements to my quality of life if I used a fitness app.	Digital Outcome Expectancy	0.784			0.662	0.338
Item_45	I believe that using a fitness app would lead to me living a healthier lifestyle.	Digital Outcome Expectancy	0.740			0.659	0.341
Item_40	I would use a fitness app, because I find exploring new health technologies exciting.	Digital Behavioural Regulation	0.740			0.614	0.386
Item_21	I would trust a new health technology with information about my health conditions.	Digital Trust	0.703			0.437	0.563
Item_39	I would use a fitness app if told to by health professionals.	Digital Behavioural Regulation	0.645			0.400	0.600
Item_16	Others would be pleased I was using a health technology.	Sociodigital Influences	0.640			0.391	0.609
Item_19	I would trust a new health technology to protect my personal information.	Digital Trust	0.637			0.449	0.551
Item_17	I would trust a new health technology with my personal details.	Digital Trust	0.631			0.438	0.562
Item_36	I would use a fitness app because using technology is part of who I am.	Digital Behavioural Regulation	0.560			0.481	0.519
Item_14	I feel like using health technology is expected these days.	Sociodigital Influences	0.465			0.226	0.774
Item_18	I would expect health technologies to have measures in place to keep my information safe.	Digital Trust	0.388			0.227	0.773
Item_12	Those close to me would help me use a new health technology if needed.	Sociodigital Influences	0.386			0.233	0.767
Item_20	I worry that using a new health technology would put me at risk of fraud.	Digital Trust				0.125	0.875
Item_8	I can troubleshoot basic issues with digital technology.	Digital Skills		0.926		0.780	0.220
Item_7	I can usually use digital technology.	Digital Skills		0.827		0.649	0.351
Item_29	I am confident I could use the advanced features of a fitness app if I wanted to.	Digital Self-Efficacy		0.772		0.628	0.372
Item_1	I would understand what a health technology was asking me to do to improve my health.	Digital Health Literacy		0.639		0.479	0.521
Item_9	I often struggle to use digital technology.	Digital Skills		0.636		0.418	0.582
Item_28	I am confident I could troubleshoot basic issues with a fitness app if I wanted to.	Digital Self-Efficacy		0.623		0.446	0.554
Item_27	I am confident I could use a fitness app without help if I wanted to.	Digital Self-Efficacy		0.622		0.483	0.517
Item_24	I am confident I could learn how to use a fitness app if I wanted to.	Digital Self-Efficacy		0.591		0.523	0.477
Item_10	I can usually adjust the settings on digital technology to suit my needs.	Digital Skills		0.578	0.511	0.716	0.284
Item_6	I can usually download apps.	Digital Skills		0.575		0.506	0.494
Item_25	I am confident I could use a fitness app to monitor my health if I wanted to.	Digital Self-Efficacy	0.341	0.454		0.450	0.550
Item_2	I can usually work out if health information online is reliable.	Digital Health Literacy		0.311		0.115	0.885
Item_3	I usually know when health information online is wrong.	Digital Health Literacy		0.305		0.092	0.908
Item_4	I would understand information about my health from health technologies.	Digital Health Literacy			0.571	0.510	0.490
Item_37	I would use a fitness app because I would feel guilty if I didn't.	Digital Behavioural Regulation			0.547	0.297	0.703
Item_32	I wouldn't use a a fitness app because I find technology frustrating.	Digital Behavioural Regulation	0.300	0.380	0.505	0.677	0.323
Item_26	I am confident I could use a fitness app regularly if I wanted to.	Digital Self-Efficacy	0.375	0.340	0.497	0.705	0.295
Item_34	I would use a fitness app because I don't want to let others down.	Digital Behavioural Regulation			0.467	0.269	0.731
Item_13	I don't have anyone in my life that could help me use a health technology.	Sociodigital Influences			0.361	0.198	0.802
Item_5	I would know which health technology was right for me.	Digital Health Literacy			0.332	0.295	0.705
Item_15	Few people I know use health technologies.	Sociodigital Influences				0.047	0.953


Factor Correlations:

Inter-Factor Correlations
	ML1	ML2	ML3
ML1	1.000	0.352	0.167
ML2	0.352	1.000	0.147
ML3	0.167	0.147	1.000


Model Fit Statistics:

Chi-square: 806.6

df: 858

p-value: 1.474695e-19

RMSEA: 0.086

TLI: 0.703

BIC: -2297.38


Cronbach's Alpha by Theoretical Sub-Construct:

Digital Health Literacy : 0.581 
Digital Skills : 0.828 
Sociodigital Influences : 0.624 
Digital Trust : 0.83 
Digital Self-Efficacy : 0.832 
Some items ( Item_34 Item_37 ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' optionDigital Behavioural Regulation : 0.892 
Digital Outcome Expectancy : 0.935

Model Fit Statistics

📊 Interpreting Model Fit Statistics

Chi-square test:
- Tests if your model fits perfectly (null hypothesis)
- Want: Non-significant (p > 0.05) for good fit
- But: Very sensitive to sample size
- often significant even with good fit

RMSEA (Root Mean Square Error of Approximation): - Measures how well model approximates population
- Excellent: < 0.05 | Good: 0.05 - 0.08 | Mediocre: 0.08 - 0.10 | Poor: > 0.10

TLI (Tucker-Lewis Index): - Compares your model to a baseline (independence) model
- Excellent: > 0.95 | Good: 0.90 - 0.95 | Acceptable: 0.85 - 0.90 | Poor: < 0.85

BIC (Bayesian Information Criterion):
- Used for model comparison (lower = better)
- Compare different factor solutions | Penalizes model complexity

💡 Interpretation Tips:
- With small samples (N=50), fit indices can be unreliable
- Focus on RMSEA and TLI over chi-square
- Poor fit might suggest: wrong number of factors, bad items, or complex structure
- At pilot stage: Note patterns, don’t over-interpret