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Intensive lifestyle interventions are effective in reducing the risk of type 2 diabetes, but the implementation of learnings from landmark studies is expensive and time consuming. The availability of digital lifestyle interventions is increasing, but evidence of their effectiveness is limited.
This randomized controlled trial (RCT) aimed to test the feasibility of a web-based diabetes prevention program (DPP) with step-dependent feedback messages versus a standard web-based DPP in people with prediabetes.
We employed a two-arm, parallel, single-blind RCT for people at high risk of developing diabetes. Patients with a hemoglobin A1c (HbA1c) level of 39-47 mmol/mol were recruited from 21 general practices in London. The intervention integrated a smartphone app delivering a web-based DPP course with SMS texts incorporating motivational interviewing techniques and step-dependent feedback messages delivered via a wearable device over 12 months. The control group received the wearable technology and access to the web-based DDP but not the SMS texts. As this was a feasibility study, the primary aim was to estimate potential sample size at different stages of the study, including the size of the target study population and the proportion of participants who consented, were randomized, and completed follow-up. We also measured the main outcomes for a full-scale RCT, namely, change in weight and physical activity at 6- and 12-month follow-ups, and secondary outcomes, including changes in the HbA1c level, blood pressure, waist circumference, waist-to-hip ratio, and lipid levels.
We enrolled 200 participants: 98 were randomized to the intervention and 102 were randomized to the control group. The follow-up rate was higher in the control group (87/102, 85.3%) than in the intervention group (69/98, 70%) at 12 months. There was no treatment effect on weight at 6 months (mean difference 0.15; 95% CI −0.93 to 1.23) or 12 months (mean difference 0.07 kg; 95% CI −1.29 to 1.44) or for physical activity levels at 6 months (mean difference −382.90 steps; 95% CI −860.65 to 94.85) or 12 months (mean difference 92.64 steps; 95% CI −380.92 to 566.20). We did not observe a treatment effect on the secondary outcomes measured at the 6-month or 12-month follow-up. For the intervention group, the mean weight was 92.33 (SD 15.67) kg at baseline, 91.34 (SD 16.04) kg at 6 months, and 89.41 (SD 14.93) kg at 12 months. For the control group, the mean weight was 92.59 (SD 17.43) kg at baseline, 91.71 (SD 16.48) kg at 6 months, and 91.10 (SD 15.82) kg at 12 months. In the intervention group, the mean physical activity was 7308.40 (SD 4911.93) steps at baseline, 5008.76 (SD 2733.22) steps at 6 months, and 4814.66 (SD 3419.65) steps at 12 months. In the control group, the mean physical activity was 7599.28 (SD 3881.04) steps at baseline, 6148.83 (SD 3433.77) steps at 6 months, and 5006.30 (SD 3681.1) steps at 12 months.
This study demonstrates that it is feasible to successfully recruit and retain patients in an RCT of a web-based DPP.
ClinicalTrials.gov NCT02919397; http://clinicaltrials.gov/ct2/show/NCT02919397
The prevalence of prediabetes is approximately 10% in the UK population [
Landmark randomized controlled trials (RCTs) have repeatedly shown that intensive face-to-face diabetes prevention programs (DPP) are effective in reducing the risk of type 2 diabetes by approximately 50% [
Components of behavior change techniques considered to be most effective in improving diet and physical activity (PA) are based on self-regulatory behaviors, such as goal setting, self-monitoring, giving feedback, utilizing social support, and motivational interviewing (MI) [
This RCT aimed to test the feasibility of a web-based DPP, consisting of a wearable technology that records PA, integrated with SMS texts based on MI techniques, and lifestyle education delivered via a smartphone app, over 12 months in participants with prediabetes. The primary aims were to assess (1) the potential size of the study population; (2) the proportion of those who consented to be screened for eligibility; (3) the proportion of those who were screened and who were eligible, consented, and randomized; (4) the proportion of those who were randomized and who completed the intervention; and (5) the proportion of those who completed the 6-month and 12-month follow-ups.
Our secondary aims were to measure the change in biomedical outcomes, including reducing weight and increasing PA, to inform the possible range of effect sizes and obtain outcome variance estimates required for sample size calculations in a full-scale trial.
This was a two-arm, parallel, single-blind RCT conducted over 12 months. The trial has been reviewed and given favorable opinion by the London City and East Research Ethics Committee (16/LO/1505).
We recruited patients from 3 clinical commissioning groups in London (Lambeth, Southwark, and Lewisham), which comprise a population of 912,687 residents, with one of the highest prevalence rates of type 2 diabetes [
Patients with an HbA1c level of 39 to 47 mmol/mol were defined as being in a prediabetes state according to the current American Diabetes Association criteria [
The exclusion criteria included diabetes (not including past history of gestational diabetes); pregnancy; planning a pregnancy or lactating during the duration of the study; severe mental illness (severe depression with suicidal ideation, psychosis, bipolar affective disorder, dementia, learning difficulties, substance problem use, or dependence); severe physical disability (eg, that would prevent any increased uptake of physical exercise); advanced active disease, such as cancer or heart failure; any other condition that requires glucose-altering drugs; BMI≥50 kg/m²; and current participation in a weight loss program or DPP. When in doubt, we sought GP confirmation of eligibility.
We collected sociodemographic data, including age, gender, postcode of residence, employment status, educational level, and self-reported ethnicity. On the basis of the participant’s postcode, we determined their indices of multiple deprivation (IMD) 2015 rank, which indicates the relative level of socioeconomic deprivation in their area [
Objective PA was measured using wearable technology (a wristband manufactured and provided by Buddi Ltd;
We collected HbA1c (mmol/mol) and lipid levels (total cholesterol, high- and low-density lipoproteins, and fasting triglycerides; all values in mmol/L). Weight was measured in light clothing, without shoes, to 0.01 kg, and height to 0.1 cm using a stadiometer (Class 3 Tanita SC240). Weight and height measurements were used to calculate the BMI (kg/m2). Waist circumference (cm) was measured horizontally halfway between the lowest rib and the upper prominence of the pelvis using a nonextensible steel tape against the bare abdomen. Hip circumference was also measured to calculate the waist-to-hip ratio. Diastolic and systolic blood pressure (BP) and resting heart rate were measured with digital Omron BP monitors (Omron M7) using standardized procedures of the average of 2 readings taken 1 min apart while seated.
Subjective PA was assessed using the International Physical Activity Questionnaire (IPAQ) [
The intervention was based on the theory of planned behavior, which states that to change behavior, people need to form an intention [
All participants were issued with a wristband (manufactured and provided by Buddi Ltd), its charger, and instructions for operating the wristband and downloading the associated study-specific smartphone app. In the baseline appointment, participants downloaded the app onto their smartphone and wirelessly connected it to the wristband via Bluetooth with the help of the researcher if needed. Participants were told that they must maintain the Bluetooth connection to facilitate the transfer of data captured by the Buddi wristband to the participants’ smartphones. This allowed participants to track their activity in close to real time via the smartphone d as well as review past activity. If any technical issues arose, participants were able to contact a researcher (who was not blinded to the intervention allocation) for technical support, and any faulty devices were replaced.
We scheduled and delivered 22 web-based sessions over 12 months targeting diet, PA, and mental resilience. The curriculum was based on the newly developed Centers for Disease Control and Prevention PreventT2 curriculum and handouts [
SMS texts were generated and delivered via the smartphone app using principles and techniques from MI to support participants in forming healthy intentions, encourage self-monitoring of lifestyle behaviors, and promote social support [
Messages targeting lifestyle behaviors encouraged
One daily message giving feedback on the activity data received from the wearable technology; the content was based on the level of activity designed to reinforce or encourage an increase in activity levels.
Responsive messages were only sent if participants proactively selected the following on the app:
The control group was provided with the Buddi wristband for the duration of the study and could access their activity data and web-based education material via the smartphone app. They received an automated message (via the smartphone app) informing them when the next educational session was available (ie, 22 messages in total), but they did not receive any other messages. This was weekly for modules 1 to 6, biweekly for modules 7 to 16, and monthly (4 weeks) for modules 17 to 22.
For feasibility parameters, the primary outcomes were proportion recruited and randomized and proportion followed up. The primary clinical outcomes were change in weight (kg) and PA (mean steps per day) from baseline to 12 months, with an interim measure at 6 months. Follow-up PA was the mean step count of the 7 days of wear leading up to and including the day of the follow-up appointment.
The secondary outcomes were a change in HbA1c levels and BP at 6 and 12 months and waist circumference, waist-to-hip ratio, and lipid levels at 12 months. The HbA1c level was analyzed as a continuous and categorical variable, with the following categories: normal (<42 mmol/mol), prediabetes (39-47 mmol/mol), and diabetes (>47 mmol/mol).
We aimed for 100 participants per arm, as this was a large enough sample to inform the practicalities of delivering the intervention, recruitment, uptake, and attrition to inform a full-scale trial rather than measures of intervention effects.
Before randomization, participants wore the Buddi wristband for 1 week to familiarize with the technology and to collect baseline activity data. All patients were offered a brief educational session on the use of the Buddi device, and an instruction manual was provided. Randomization of participants was conducted by the data manager from an independent clinical trials unit using computer-generated randomization blocks of random sizes and stratified by surgery in a 1:1 ratio. Allocation concealment of the randomization list was held in a password-locked computer. This was an open-label study, but outcome assessors, laboratory technicians, and researchers entering and scoring the data were blinded to patients’ allocations.
The full statistical analysis plan for this study is provided in
Participant flow diagram.
The baseline characteristics of the participants are presented in
Data were available for 192 participants (92 in the intervention arm). Of these 192, a total of 80 (80.0%) and 60 (65.2%) participants were adherent to the intervention in the intervention and control arms, respectively (X21=4.6;
Baseline characteristics.
Baseline characteristics | Intervention (n=98)a | Control (n=102)b | Total (N=200) | ||||
Age (years), mean (SD) | 51.76 (7.68) | 52.78 (8.20) | 52.28 (7.94) | ||||
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Female | 56 (57) | 51 (50.0) | 106 (53.0) | |||
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White | 28 (28) | 35 (34.3) | 63 (31.5) | |||
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African or African Caribbean | 50 (51) | 58 (56.9) | 108 (54.0) | |||
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Asian | 16 (16) | 4 (3.9) | 20 (10.0) | |||
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Other | 4 (4) | 5 (4.9) | 9 (4.5) | |||
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No formal qualifications | 3 (3) | 5 (5.2) | 8 (4.1) | |||
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GCSEc or equivalent | 29 (29) | 23 (23.7) | 52 (26.8) | |||
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A level or higher | 65 (67) | 69 (71.1) | 134 (69.1) | |||
Currently employed, full- or part-time, n (%) | 74 (77) | 78 (78.8) | 152 (77.9) | ||||
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Married/cohabiting | 61 (62) | 59 (57.8) | 120 (60.0) | |||
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Separated/divorced/widowed | 12 (12) | 13 (12.7) | 25 (12.5) | |||
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Single | 25 (25) | 30 (29.4) | 55 (27.5) | |||
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1—most deprived | 33 (33) | 36 (35.3) | 69 (34.5) | |||
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2 | 32 (32) | 38 (37.3) | 70 (35.0) | |||
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3 | 21 (21) | 19 (18.6) | 40 (20.0) | |||
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4 | 11 (11) | 8 (7.8) | 19 (9.5) | |||
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5—least deprived | 1 (1) | 1 (1.0) | 2 (1.0) | |||
Family history of diabetes, n (%) | 52 (53) | 51 (50.0) | 103 (51.5) | ||||
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Current smoker | 13 (13) | 10 (9.8) | 23 (11.5) | |||
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Ex-smoker | 32 (32) | 41 (40.2) | 73 (36.5) | |||
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Nonsmoker | 53 (54) | 51 (50.0) | 104 (52.0) | |||
Number of cigarettes per day for current smokers, mean (SD) | 7.50 (5.76) | 6.40 (5.21) | 7.02 (5.43) | ||||
PHQ-9e depression score, mean (SD) | 4.04 (4.61) | 4.25 (4.01) | 4.15 (4.31) | ||||
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Abstainer (0) | 22 (22) | 21 (20.6) | 43 (21.5) | |||
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Low risk (1-7) | 71 (72) | 69 (67.6) | 140 (70.0) | |||
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Possibly harmful (≥8) | 5 (5) | 12 (11.8) | 17 (8.5) | |||
IPAQg total physical activity, METh minutes/week, median (IQR) | 2264.18 (2311.16) | 2647.01 (2715.67) | 2459.43 (2526.55) | ||||
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Precontemplation (<8) | 22 (22.) | 12 (11.8) | 34 (17.1) | |||
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Contemplation (8-11) | 54 (55) | 70 (68.6) | 124 (62.3) | |||
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Preparation or action (>11) | 21 (21) | 20 (19.6) | 41 (20.6) | |||
SEEj self-efficacy score, mean (SD) | 55.15 (22.11) | 54.33 (21.95) | 54.73 (21.97) |
aNumber of missing cases for the intervention arm are as follows: highest qualification (n=1), currently employed (n=3), PHQ-9 (n=1), URICA (n=1), and SEE (n=2).
bNumber of missing cases for the control arm are as follows: highest qualification (n=5) and currently employed (n=2).
cGCSE: general certificate of secondary education.
dIMD: indices of multiple deprivation.
ePHQ-9: patient health questionnaire-9.
fAUDIT: alcohol use disorders identification test.
gIPAQ: international physical activity questionnaire.
hMET: metabolic equivalent of the task.
iURICA: University of Rhode Island change assessment scale.
jSEE: self-efficacy for exercise.
When categorizing participants’ metabolic status based on their HbA1c values, 3 (3.5%) and 5 (7.3%) in intervention group and 2 (2.2%) and 6 (6.9%) in the control group met the cut-off for diabetes (>47 mmol/mol) at 6 and 12 months, respectively. Participants with HbA1c >47 mmol/mol were referred to the GP. Participants were informed of their results via telephone and told to contact their GP. A total of 4 (4.7%) and 9 (10.1%) participants returned to the normal range (<39 mmol/mol) in the intervention and control arms, respectively, at 6 months. At 12 months, 0 and 4 (4.6%) participants were in the normal range in the intervention and control arms, respectively. There was no difference in the proportion of participants who developed type 2 diabetes or who returned to normal HbA1c levels between the 2 groups at 6 months (X22=2.0;
Summary of primary and secondary outcomes and pairwise comparisons.
Outcomes and time | Intervention arm | Control arm | Mean difference (95% CI) | |||||
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n | Mean (SD) | n | Mean (SD) |
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Baseline | 98 | 92.33 (15.67) | 102 | 92.59 (17.43) | —a | ||
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6-month follow-up | 85 | 91.34 (16.04) | 89 | 91.71 (16.48) | 0.15 (−0.93 to 1.23)b | ||
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12-month follow-up | 69 | 89.41 (14.93) | 87 | 91.10 (15.82) | 0.07 (−1.29 to 1.44) | ||
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Baseline | 87 | 7308.40 (4911.93) | 93 | 7599.28 (3881.04) | — | ||
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6-month follow-up | 36 | 5008.76 (2733.22) | 51 | 6148.83 (3433.77) | −382.90 (−860.65 to 94.85) | ||
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12-month follow-up | 18 | 4814.66 (3419.65) | 31 | 5006.30 (3681.1) | 92.64 (−380.92 to 566.20) | ||
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Baseline | 98 | 82.92 (10.68) | 102 | 81.47 (8.96) | — | ||
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6-month follow-up | 82 | 81.41 (10.19) | 89 | 83.22 (8.46) | −2.24 (−4.54 to 0.06) | ||
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12-month follow-up | 68 | 83.03 (10.33) | 87 | 83.87 (9.06) | −1.61 (−3.93 to 0.70) | ||
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Baseline | 98 | 125.51 (17.39) | 102 | 124.52 (12.19) | — | ||
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6-month follow-up | 82 | 124.15 (16.33) | 89 | 127.33 (13.30) | 3.50 (−7.05 to 0.05) | ||
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12-month follow-up | 68 | 125.37 (16.07) | 87 | 127.54 (14.16) | −2.62 (−6.37 to 1.12) | ||
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Baseline | 97 | 4.02 (0.99) | 102 | 4.14 (1.02) | — | ||
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12-month follow-up | 68 | 4.06 (1.20) | 86 | 3.97 (1.04) | 0.11 (−0.08 to 0.30) | ||
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Baseline | 98 | 42.27 (2.32) | 102 | 42.29 (1.98) | — | ||
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6-month follow-up | 85 | 42.12 (2.44) | 89 | 41.82 (3.05) | 0.20 (−0.50 to 0.91) | ||
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12-month follow-up | 68 | 44.06 (2.31) | 87 | 43.54 (2.68) | 0.53 (−0.19 to 1.25) | ||
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Baseline | 97 | 1.39 (0.33) | 102 | 1.34 (0.33) | — | ||
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12-month follow-up | 68 | 1.39 (0.32) | 86 | 1.38 (0.31) | 0.00 (−0.07 to 0.06) | ||
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Baseline | 96 | 3.33 (0.84) | 101 | 3.28 (0.83) | — | ||
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12-month follow-up | 67 | 3.30 (0.85) | 85 | 3.28 (0.85) | 0.02 (−0.18 to 0.23) | ||
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Baseline | 97 | 5.35 (0.96) | 102 | 5.30 (0.93) | — | ||
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12-month follow-up | 68 | 5.37 (1.03) | 86 | 5.28 (0.99) | 0.10 (−0.11 to 0.31) | ||
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Baseline | 97 | 1.39 (1.14) | 102 | 1.39 (0.86) | — | ||
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12-month follow-up | 68 | 1.72 (3.13) | 86 | 1.35 (0.84) | 0.39 (−0.02 to 0.81) | ||
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Baseline | 97 | 103.31 (12.21) | 100 | 103.92 (12.32) | — | ||
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12-month follow-up | 69 | 96.73 (11.69) | 86 | 97.38 (10.99) | −0.60 (−2.45 to 1.26) | ||
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Baseline | 97 | 0.92 (0.07) | 100 | 0.93 (0.08) | — | ||
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12-month follow-up | 69 | 0.91 (0.08) | 86 | 0.90 (0.08) | 0.00 (−0.02 to 0.02) |
aBaseline comparisons are not done, as per the statistical analysis plan noted above.
bPairwise comparison outputs were calculated by subtracting the control arm from the intervention arm, so a negative value indicates the control arm had a higher mean.
cPA: physical activity.
dNumber of days included in the step count calculations is given in
eBP: blood pressure.
fHDL: high-density lipoprotein.
gLDL: low-density lipoprotein.
The fixed and random effects of the mixed effects models for weight and PA are presented in
Our sensitivity analyses adjusting for baseline characteristics did not alter our conclusions of the primary outcome analysis above. Readiness to change (URICA stage) did not moderate the treatment-by-time effect for weight (
Our analysis of responders to the control or intervention arm indicated that improvements in weight and/or PA were associated with baseline smoking status (X22=11.6;
Testing the associations between IPAQ scores and mean step counts revealed a significant positive correlation between the intervention arm’s IPAQ total activity score and step counts at baseline only (
We have demonstrated that conducting an RCT that tested web-based DPP delivered via a wearable technology and a smartphone app is feasible in terms of participation and retention of study patients over a period of 12 months.
We successfully reached our target sample size in 36 weeks, with a recruitment rate of 5 to 6 patients per week per full-time research worker. For full-scale RCT testing the intervention with an expected effect size of 0.1 and 90% power, we estimate recruitment will take about 3.5 years per research worker or just over 1 year for 3 research workers (
We piloted the statistical plan that we would anticipate using for a full-scale RCT, which appeared to be valid, although hypothesis testing was only a secondary aim. The only significant difference observed was that those in the intervention group had lower PA levels at 6 months compared with those in the control group, but they were also less likely to complete the intervention and more likely to be noncontactable or have withdrawn at follow-up.
An important difference between our study and previous RCTs [
A key strength is that this is the first feasibility study in the United Kingdom, which tests an automated web-based DPP designed to mirror the landmark face-to-face DPPs. The sample was representative of the ethnic and social diversity common in prediabetes. We noted that there was variation by GP surgery in response, and this suggests that clustering should be considered in any full-scale RCT.
An important observation is the importance of sustaining the functionality of the technology. Despite a prior
Participant adherence to the intervention could not be comprehensively documented as the technology for this did not exist; therefore, we were not able to capture how the participants used the app, particularly if they accessed the DPP education materials (and for how long) or if they read the SMS messages sent to them.
The follow-up rates for the primary outcomes were, on average, 75%, and predictors of missing data included higher BMI, presence of depressive symptoms, and current smokers (ie, those more at risk of developing type 2 diabetes and increasing the risk of underestimation).
We assessed the degree to which the wristband-derived step counts (ie, objectively measured PA) were associated with a validated self-reported measure of PA levels. We observed that the 2 measures only corresponded to a limited extent in the control arm, with step counts being weakly and positively correlated with total self-reported activity at 2 time points. The self-reported scores were not associated with step counts in the intervention arm. There are several possible reasons for this discrepancy, one being that these participants disengaged from the intervention (ie, wearing the wristband because of the technical problems or the higher intensity of messages).
The responses of the participants to our implementation processes questionnaire were generally favorable of the intervention; however, they did note several key areas for improvement for a full-scale RCT. These included improving the wristband’s clasp, offering the wristband in different styles, and improving the app’s accessibility (eg, offering the educational material in other digital formats) and formatting (eg, improving the readability and precision of the activity graphs and personalizing the content and frequency of messages).
This study found that there is a sufficiently large target population of patients for screening and a reasonably good participation rate (ie, patients are keen to receive support for diabetes prevention). Ensuring an optimum balance in the intensity of information sent and the functionality of the technology are potential key components to consider for a full-scale RCT.
Number of days used in calculating step counts.
Full statistical analysis plan.
Fixed and random effects of the mixed effects models for weight and PA.
Analysis of responders.
Testing associations between the International Physical Activity Questionnaire scores and mean step count.
Sample size calculations for a future full-scale randomized controlled trial.
CONSORT-eHEALTH (V 1.6.1).
alcohol use disorders identification test
blood pressure
Centers for Disease Control and Prevention
diabetes prevention program
Egton Medical Information Systems
general practitioner
hemoglobin A1c
indices of multiple deprivation
International Physical Activity Questionnaire
King’s College London
motivational interviewing
patient health questionnaire-9
randomized controlled trial
University of the Rhode Island change assessment scale
Funding for this study was provided by Innovate UK (ID: 21370). The funder had no role in the design of the study or the collection, analysis, and interpretation of data, and the funder had no part in writing the manuscript. Buddi Ltd provided the wristband and worked with this King’s College London (KCL) research team to develop the smartphone app. The paper does not necessarily represent the views of Innovate UK/Buddi. KI is funded in part by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London Maudsley Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. KI has received honorarium for educational lectures from Janssen, Sanofi, Eli Lilly, and Novo Nordisk. The authors thank Prof Daniel Stahl (Department of Biostatistics, Institute of Psychiatry, Psychology & Neuroscience, KCL) for feedback on the statistical plan in the protocol. The other authors have none to declare.
None declared.