Promoting physical activity in a multi-ethnic population at high risk of diabetes: the 48-month PROPELS randomised controlled trial

A systematic review on eHealth technology personalization approaches

Despite the widespread use of personalization of eHealth technologies, there is a lack of comprehensive understanding regarding its application. This systematic review aims to bridge this gap by identifying and clustering different personalization approaches based on the type of variables used for user segmentation and the adaptations to the eHealth technology and examining the role of computational methods in the literature. From the 412 included reports, we identified 13 clusters of personalization approaches, such as behavior + channeling and environment + recommendations. Within these clusters, 10 computational methods were utilized to match segments with technology adaptations, such as classification-based methods and reinforcement learning. Several gaps were identified in the literature, such as the limited exploration of technology-related variables, the limited focus on user interaction reminders, and a frequent reliance on a single type of variable for personalization. Future research should explore leveraging technology-specific features to attain individualistic segmentation approaches.

Effect of sustained decreases in sedentary time and increases in physical activity on liver enzymes and indices in type 2 diabetes

Background

Current guidelines for nonalcoholic fatty liver disease (NAFLD) recommend high volumes and/or intensities of physical activity (PA), the achievement of which generally requires participation in supervised exercise training programs that however are difficult to implement in routine clinical practice. Conversely, counselling interventions may be more suitable, but result in only modest increases in moderate-to-vigorous-intensity PA (MVPA). This study assessed whether a counseling intervention for increasing PA and decreasing sedentary time (SED-time) is effective in improving NAFLD markers in people with type 2 diabetes.

Methods

Three-hundred physically inactive and sedentary patients were randomized 1:1 to receive one-month theoretical and practical counseling once-a-year (intervention group) or standard care (control group) for 3 years. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyltranspeptidase (γGT) levels were measured and fatty liver index (FLI), hepatic steatosis index (HSI), and visceral adiposity index (VAI) were calculated. Total PA volume, light-intensity PA (LPA), moderate-to-vigorous-intensity PA (MVPA), and SED-time were objectively measured by an accelerometer.

Results

Throughout the 3-year period, NAFLD markers did not change in the control group, whereas ALT, γGT, FLI, and HSI decreased in the intervention group, with significant between-group differences, despite modest MVPA increases, which however were associated with larger decrements in SED-time and reciprocal increments in LPA. Mean changes in NAFLD markers varied according to quartiles of (and correlated with) changes in MVPA (all markers) and SED-time, LPA, and PA volume (ALT, γGT, and HSI). Mean changes in MVPA or PA volume were independent predictors of changes in NAFLD markers. When included in the models, change in cardiorespiratory fitness and lower body muscle strength were independently associated with some NAFLD markers.

Conclusion

A behavior change involving all domains of PA lifestyle, even if insufficient to achieve the recommended MVPA target, may provide beneficial effects on NAFLD markers in people with type 2 diabetes.

Exploring the Causal Relationship Between Modifiable Exposures and Diabetes Mellitus: A Two-Sample Mendelian Randomization Analysis

Background Observational studies link lifestyle factors to diabetes, but confounding limits causal inference. This study employed Mendelian randomization (MR) to investigate the potential causal effects of major dietary, obesity, smoking, and physical activity exposures on diabetes risk. Methods A two-sample MR framework integrated FinnGen and United Kingdom Biobank (UKB) data. Genetic instruments for diet (fruits, vegetables, cheese), smoking (initiation, intensity, maternal), body mass index (BMI), and physical activity came from various consortia (n=64, 949-632, 802). Associations with diabetes odds were assessed using inverse-variance weighted analysis. Results Fruit and cheese intake and physical activity per standard deviation increase causally reduced diabetes risk in both cohorts. Conversely, smoking initiation, maternal smoking around birth, and BMI per standard deviation increase causally increased diabetes risk in both cohorts. Coffee increased diabetes risk only in FinnGen, whereas smoking intensity increased diabetes risk only in UKB. Conclusion This study provides robust evidence that modifiable lifestyle factors may have causal effects on diabetes risk. Fruit, cheese, and physical activity may protect against diabetes, whereas smoking, maternal smoking, and higher BMI appear to increase risk. Findings support public health interventions targeting diet, physical activity, smoking cessation, and healthy weight to combat the global diabetes epidemic.

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED)

BACKGROUND

The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour.

METHODS

The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form.

RESULTS

The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence.

CONCLUSIONS

The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Cost-effectiveness analysis of two interventions to promote physical activity in a multiethnic population at high risk of diabetes: an economic evaluation of the 48-month PROPELS randomized controlled trial

INTRODUCTION

Physical activity (PA) is protective against type 2 diabetes (T2D). However, data on pragmatic long-term interventions to reduce the risk of developing T2D via increased PA are lacking. This study investigated the cost-effectiveness of a pragmatic PA intervention in a multiethnic population at high risk of T2D.

MATERIALS AND METHODS

We adapted the School for Public Health Research diabetes prevention model, using the PROPELS trial data and analyses of the NAVIGATOR trial. Lifetime costs, lifetime quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated for each intervention (Walking Away (WA) and Walking Away Plus (WA+)) versus usual care and compared with National Institute for Health and Care Excellence's willingness-to-pay of £20 000-£30 000 per QALY gained. We conducted scenario analyses on the outcomes of the PROPELS trial data and a threshold analysis to determine the change in step count that would be needed for the interventions to be cost-effective.

RESULTS

Estimated lifetime costs for usual care, WA, and WA+ were £22 598, £23 018, and £22 945, respectively. Estimated QALYs were 9.323, 9.312, and 9.330, respectively. WA+ was estimated to be more effective and cheaper than WA. WA+ had an ICER of £49 273 per QALY gained versus usual care. In none of our scenario analyses did either WA or WA+ have an ICER below £20 000 per QALY gained. Our threshold analysis suggested that a PA intervention costing the same as WA+ would have an ICER below £20 000/QALY if it were to achieve an increase in step count of 500 steps per day which was 100% maintained at 4 years.

CONCLUSIONS

We found that neither WA nor WA+ was cost-effective at a limit of £20 000 per QALY gained. Our threshold analysis showed that interventions to increase step count can be cost-effective at this limit if they achieve greater long-term maintenance of effect.

TRIAL REGISTRATION NUMBER

ISRCTN registration: ISRCTN83465245: The PRomotion Of Physical activity through structuredEducation with differing Levels of ongoing Support for those with pre-diabetes (PROPELS)https://doi.org/10.1186/ISRCTN83465245.

Effectiveness of Computer-Tailored Health Communication in Increasing Physical Activity in People With or at Risk of Long-Term Conditions: Systematic Review and Meta-Analysis

BACKGROUND

Regular physical activity (PA) is beneficial for enhancing and sustaining both physical and mental well-being as well as for the management of preexisting conditions. Computer-tailored health communication (CTHC) has been shown to be effective in increasing PA and many other health behavior changes in the general population. However, individuals with or at risk of long-term conditions face unique barriers that may limit the applicability of CTHC interventions to this population. Few studies have focused on this cohort, providing limited evidence for the effectiveness of CTHC in promoting PA.

OBJECTIVE

This systematic review and meta-analysis aims to assess the effectiveness of CTHC in increasing PA in individuals with or at risk of long-term conditions.

METHODS

A systematic review and meta-analysis were conducted to evaluate the effect of CTHC in increasing PA in people with or at risk of long-term conditions. Hedges g was used to calculate the mean effect size. The total effect size was pooled and weighted using inverse variance. When possible, potential moderator variables were synthesized, and their effectiveness was evaluated by subgroups analysis with Q test for between-group heterogeneity Qb. Potential moderator variables included behavior change theories and models providing the fundamental logic for CTHC design, behavior change techniques and tailoring strategies to compose messages, and computer algorithms to achieve tailoring. Several methods were used to examine potential publication bias in the results, including the funnel plot, Egger test, Begg test, fail-safe N test, and trim-and-fill method.

RESULTS

In total, 24 studies were included in the systematic review for qualitative analysis and 18 studies were included in the meta-analysis. Significant small to medium effect size values were found when comparing CTHC to general health information (Hedges g=0.16; P<.001) and to no information sent to participants (Hedges g=0.29; P<.001). Half of the included studies had a low to moderate risk of bias, and the remaining studies had a moderate to high risk of bias. Although the results of the meta-analysis indicated no evidence of publication bias, caution is required when drawing definitive conclusions due to the limited number of studies in each subgroup (N≤10). Message-tailoring strategies, implementation strategies, behavior change theories and models, and behavior change techniques were synthesized from the 24 studies. No strong evidence was found from subgroup analyses on the effectiveness of using particular behavior change theories and models or from using particular message-tailoring and implementation strategies.

CONCLUSIONS

This study demonstrates that CTHC is effective in increasing PA for people with or at risk of long-term conditions, with significant small to medium effects compared with general health information or no information. Further studies are needed to guide design decisions for maximizing the effectiveness of CTHC.

Physical Activity as a Lifestyle Modification in Patients With Multiple Comorbidities: Emphasizing More on Obese, Prediabetic, and Type 2 Diabetes Mellitus Patients

In this research, physical activity (PA) was shown to be inversely associated with the incidence of diabetes. This emphasizes the relevance of PA in diabetes prevention lifestyle intervention initiatives and encourages healthcare practitioners to advise high-risk patients on a healthy lifestyle combining PA for the reduction of weight in prediabetic, obese, and type 2 diabetes mellitus (T2DM) patients. The link between PA and diabetes was stronger in people with moderate or higher baseline PA, reflecting national recommendations that imply increasing activity levels may provide larger advantages for those who are comparatively less active. An intensive lifestyle intervention of eating healthier foods and increasing PA resulted in an effective decrease in weight and waist circumference after one year, which has high potential in the long term to prevent T2DM and different comorbidities such as obesity. Studies such as PRomotion Of Physical activity through structured Education with differing Levels of ongoing Support for those with prediabetes (PROPELS) demonstrate that the combination of PA interventions with telemedicine follow-ups results in ambulatory activity changes in the first year, but these changes do not last longer than four years. Acute PA practicing regularly will reduce postprandial glucose excursions. However, it is unknown what type of PA routine will benefit the most from reducing postprandial glucose levels. There are no discernible variations in the effects of different disciplines of training on glucose levels, mainly when the data are compared across time. The combination of a healthy diet and lifestyle with programs based on diabetes prevention results in comparable and clinically significant mean weight reduction with cardiometabolic advantages. Based on the reviewed and cited studies, PA in patients at high risk of T2DM and obese and non-obese patients with T2DM results in favorable outcomes in the first few months. There is a large gap in investigations of the effects of PA in these patients and the benefits of other lifestyle modifications in long-term-based studies. However, in this study, we emphasize the importance of lifestyle modifications, putting in perspective principally the PA that the majority of patients with comorbidities do not practice, especially those with obesity, prediabetes, and T2DM. Thus, it would be necessary to conduct long-interval studies such as randomized clinical trials, where a better outcome can be given about intervals based on daily exercise times and the type of exercise in conjunction with diets that will have the greatest benefit, focusing more on the subjects that our research mentioned.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial

BACKGROUND

The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking.

METHODS

We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months.

DISCUSSION

The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05351359, 28/04/2022).

Recruitment and characteristics of participants in trials of physical activity for adults aged 45 years and above in general practice: a systematic review

BACKGROUND

General practice is well situated to promote physical activity (PA), but with PA levels declining after 45 years of age, often those who are most likely to benefit from interventions tend to be the least likely recruited to participate in research.

AIMS AND RATIONALE

The aim of this study was to investigate recruitment and reporting of participant demographics in PA trials for adults aged 45 years and above. Specific objectives were: (i) to examine the reporting of demographics of participants; (ii) to investigate the strategies used to recruit these participants; and, (iii) to examine the efficiency of recruitment strategies.

METHODS

Seven databases were searched, including: PubMed, CINAHL, the Cochrane Library Register of Controlled Trials, Embase, Scopus, PsycINFO, and Web of Science. Only randomized control trials involving adults 45 years old or older recruited through primary care were included. The PRISMA framework for systematic review was followed, which involved 2 researchers independently conducting title, abstract, and full article screening. Tools for data extraction and synthesis were adapted from previous work on inclusivity in recruitment.

RESULTS

The searches retrieved 3,491 studies of which 12 were included for review. Sample size of the studies ranged from 31 to 1,366, with a total of 6,042 participants of which 57% were female. Of 101 participating practices, 1 was reported as rural. Reporting of recruitment lacked detail-only 6 studies outlined how practices were recruited. 11/12 studies involved a database or chart review to identify participants that met the inclusion criteria, followed by a letter of invitation sent to those people. The studies with higher recruitment efficiency ratios each employed more than 1 recruitment strategy, e.g. opportunistic invitations and telephone calls.

CONCLUSION

This systematic review has presented deficits in the reporting of both demographics and recruitment. Future research should aim for a standardized approach to reporting.

Sustained decreases in sedentary time and increases in physical activity are associated with preservation of estimated β-cell function in individuals with type 2 diabetes

AIMS

In the Italian Diabetes and Exercise Study_2, a counselling intervention produced modest but sustained increments in moderate-to vigorous-intensity physical activity (MVPA), with reallocation of sedentary-time (SED-time) to light-intensity physical activity (LPA). This post hoc analysis evaluated the impact of intervention on estimated β-cell function and insulin sensitivity.

METHODS

Patients with type 2 diabetes were randomized to one-month counselling once-a-year or standard care for 3 years. The HOmeostatic Model Assessment-2 (HOMA-2) method was used for estimating indices of β-cell function (HOMA-B%), insulin sensitivity (HOMA-S%), and insulin resistance (HOMA-IR); the disposition index (DI) was estimated as HOMA-β%/HOMA-IR; MVPA, LPA, and SED-time were objectively measured by accelerometer.

RESULTS

HOMA-B% and DI decreased in control group, whereas HOMA-B% remained stable and DI increased in intervention group. Between-group differences were significant for almost all insulin secretion and sensitivity indices. Changes in HOMA-B% and DI correlated with SED-time, MVPA and LPA. Changes in HOMA-B%, DI, and all indices were independently predicted by changes in SED-time (or LPA), MVPA, and BMI (or waist circumference), respectively.

CONCLUSIONS

In individuals with type 2 diabetes, increasing MVPA, even without achieving the recommended target, is effective in maintaining estimated β-cell function if sufficient amounts of SED-time are reallocated to LPA.

Do physical activity interventions combining self-monitoring with other components provide an additional benefit compared with self-monitoring alone? A systematic review and meta-analysis

Objective

To determine the net effect of different physical activity intervention components on step counts in addition to self-monitoring.

Design

A systematic review with meta-analysis and meta-regression.

Data sources

Five databases (PubMed, Scopus, Web of Science, ProQuest and Discus) were searched from inception to May 2022. The database search was complemented with backward and forward citation searches and search of the references from relevant systematic reviews.

Eligibility criteria

Randomised controlled trials comparing an intervention using self-monitoring (active control arm) with an intervention comprising the same treatment PLUS any additional component (intervention arm).

Data extraction and synthesis

The effect measures were mean differences in daily step count. Meta-analyses were performed using random-effects models, and effect moderators were explored using univariate and multivariate meta-regression models.

Results

Eighty-five studies with 12 057 participants were identified, with 75 studies included in the meta-analysis at postintervention and 24 at follow-up. At postintervention, the mean difference between the intervention and active control arms was 926 steps/day (95% CI 651 to 1201). At a follow-up, the mean difference was 413 steps/day (95% CI 210 to 615). Interventions with a prescribed goal and involving human counselling, particularly via phone/video calls, were associated with a greater mean difference in the daily step count than interventions with added print materials, websites, smartphone apps or incentives.

Conclusion

Physical activity interventions that combine self-monitoring with other components provide an additional modest yet sustained increase in step count compared with self-monitoring alone. Some forms of counselling, particularly remote phone/video counselling, outperformed other intervention components, such as websites and smartphone apps.

PROSPERO registered number

CRD42020199482.

Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials

BACKGROUND

Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers.

OBJECTIVE

This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences.

METHODS

We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI.

RESULTS

Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day.

CONCLUSIONS

In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity.

TRIAL REGISTRATION

PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012.

The effectiveness of the Structured Health Intervention For Truckers (SHIFT): a cluster randomised controlled trial (RCT)

BACKGROUND

Long distance heavy goods vehicle (HGV) drivers exhibit higher than nationally representative rates of obesity, and obesity-related co-morbidities, and are underserved in terms of health promotion initiatives. The purpose of this study was to evaluate the effectiveness of the multicomponent 'Structured Health Intervention For Truckers' (SHIFT), compared to usual care, at 6- and 16-18-month follow-up.

METHODS

We conducted a two-arm cluster RCT in transport sites throughout the Midlands, UK. Outcome measures were assessed at baseline, at 6- and 16-18-month follow-up. Clusters were randomised (1:1) following baseline measurements to either the SHIFT arm or usual practice control arm. The 6-month SHIFT programme included a group-based interactive 6-h education and behaviour change session, health coach support and equipment provision (Fitbit® and resistance bands/balls to facilitate a 'cab workout'). The primary outcome was device-assessed physical activity (mean steps/day) at 6 months. Secondary outcomes included the following: device-assessed sitting, physical activity intensity and sleep; cardiometabolic health, diet, mental wellbeing and work-related psychosocial variables. Data were analysed using mixed-effect linear regression models using a complete-case population.

RESULTS

Three hundred eighty-two HGV drivers (mean ± SD age: 48.4 ± 9.4 years, BMI: 30.4 ± 5.1 kg/m², 99% male) were recruited across 25 clusters (sites) and randomised into either the SHIFT (12 clusters, n = 183) or control (13 clusters, n = 199) arms. At 6 months, 209 (55%) participants provided primary outcome data. Significant differences in mean daily steps were found between groups, in favour of the SHIFT arm (adjusted mean difference: 1008 steps/day, 95% CI: 145-1871, p = 0.022). Favourable differences were also seen in the SHIFT group, relative to the control group, in time spent sitting (- 24 mins/day, 95% CI: - 43 to - 6), and moderate-to-vigorous physical activity (6 mins/day, 95% CI: 0.3-11). Differences were not maintained at 16-18 months. No differences were observed between groups in the other secondary outcomes at either follow-up.

CONCLUSIONS

The SHIFT programme led to a potentially clinically meaningful difference in daily steps, between trial arms, at 6 months. Whilst the longer-term impact is unclear, the programme offers potential to be incorporated into driver training courses to promote activity in this at-risk, underserved and hard-to-reach essential occupational group.

TRIAL REGISTRATION

ISRCTN10483894 (date registered: 01/03/2017).

Behavioural interventions to promote physical activity in a multiethnic population at high risk of diabetes: PROPELS three-arm RCT

BACKGROUND

Type 2 diabetes is a leading cause of mortality globally and accounts for significant health resource expenditure. Increased physical activity can reduce the risk of diabetes. However, the longer-term clinical effectiveness and cost-effectiveness of physical activity interventions in those at high risk of type 2 diabetes is unknown.

OBJECTIVES

To investigate whether or not Walking Away from Diabetes (Walking Away) - a low-resource, 3-hour group-based behavioural intervention designed to promote physical activity through pedometer use in those with prediabetes - leads to sustained increases in physical activity when delivered with and without an integrated mobile health intervention compared with control.

DESIGN

Three-arm, parallel-group, pragmatic, superiority randomised controlled trial with follow-up conducted at 12 and 48 months.

SETTING

Primary care and the community.

PARTICIPANTS

Adults whose primary care record included a prediabetic blood glucose measurement recorded within the past 5 years [HbA_1c ≥ 42 mmol/mol (6.0%), < 48 mmol/mol (6.5%) mmol/mol; fasting glucose ≥ 5.5 mmol/l, < 7.0 mmol/l; or 2-hour post-challenge glucose ≥ 7.8 mmol/l, < 11.1 mmol/l] were recruited between December 2013 and February 2015. Data collection was completed in July 2019.

INTERVENTIONS

Participants were randomised (1 : 1 : 1) using a web-based tool to (1) control (information leaflet), (2) Walking Away with annual group-based support or (3) Walking Away Plus (comprising Walking Away, annual group-based support and a mobile health intervention that provided automated, individually tailored text messages to prompt pedometer use and goal-setting and provide feedback, in addition to biannual telephone calls). Participants and data collectors were not blinded; however, the staff who processed the accelerometer data were blinded to allocation.

MAIN OUTCOME MEASURES

The primary outcome was accelerometer-measured ambulatory activity (steps per day) at 48 months. Other objective and self-reported measures of physical activity were also assessed.

RESULTS

A total of 1366 individuals were randomised (median age 61 years, median body mass index 28.4 kg/m², median ambulatory activity 6638 steps per day, women 49%, black and minority ethnicity 28%). Accelerometer data were available for 1017 (74%) and 993 (73%) individuals at 12 and 48 months, respectively. The primary outcome assessment at 48 months found no differences in ambulatory activity compared with control in either group (Walking Away Plus: 121 steps per day, 97.5% confidence interval -290 to 532 steps per day; Walking Away: 91 steps per day, 97.5% confidence interval -282 to 463). This was consistent across ethnic groups. At the intermediate 12-month assessment, the Walking Away Plus group had increased their ambulatory activity by 547 (97.5% confidence interval 211 to 882) steps per day compared with control and were 1.61 (97.5% confidence interval 1.05 to 2.45) times more likely to achieve 150 minutes per week of objectively assessed unbouted moderate to vigorous physical activity. In the Walking Away group, there were no differences compared with control at 12 months. Secondary anthropometric, biomechanical and mental health outcomes were unaltered in either intervention study arm compared with control at 12 or 48 months, with the exception of small, but sustained, reductions in body weight in the Walking Away study arm (≈ 1 kg) at the 12- and 48-month follow-ups. Lifetime cost-effectiveness modelling suggested that usual care had the highest probability of being cost-effective at a threshold of £20,000 per quality-adjusted life-year. Of 50 serious adverse events, only one (myocardial infarction) was deemed possibly related to the intervention and led to the withdrawal of the participant from the study.

LIMITATIONS

Loss to follow-up, although the results were unaltered when missing data were replaced using multiple imputation.

CONCLUSIONS

Combining a physical activity intervention with text messaging and telephone support resulted in modest, but clinically meaningful, changes in physical activity at 12 months, but the changes were not sustained at 48 months.

FUTURE WORK

Future research is needed to investigate which intervention types, components and features can help to maintain physical activity behaviour change over the longer term.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN83465245.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 77. See the NIHR Journals Library website for further project information.

Relationships of Changes in Physical Activity and Sedentary Behavior With Changes in Physical Fitness and Cardiometabolic Risk Profile in Individuals With Type 2 Diabetes: The Italian Diabetes and Exercise Study 2 (IDES_2)

OBJECTIVE

In the Italian Diabetes and Exercise Study_2 (IDES_2), behavioral counseling promoted a sustained increase in physical activity (PA) volume (+3.3 MET h ⋅ week-1), moderate- to vigorous-intensity PA (MVPA) (+6.4 min ⋅ day-1), and light-intensity PA (LPA) (+0.8 h ⋅ day-1) and decrease in sedentary time (SED-time) (-0.8 h ⋅ day-1). Here, we investigated the relationships of changes in PA/SED-time with changes in physical fitness and cardiometabolic risk profile in individuals with type 2 diabetes.

RESEARCH DESIGN AND METHODS

In this 3-year randomized clinical trial, 300 physically inactive and sedentary patients were randomized 1:1 to receive 1-month theoretical and practical counseling once a year or standard care. Changes in physical fitness and cardiovascular risk factors/scores according to quartiles of accelerometer-measured changes in PA/SED-time were assessed, together with univariate and multivariable associations between these parameters, in the whole cohort and by study arm.

RESULTS

Physical fitness increased and HbA1c and coronary heart disease 10-year risk scores decreased with quartiles of MVPA and SED-time change. In quartile IV of MVPA increase and SED-time decrease, cardiorespiratory fitness increased by 5.23 and 4.49 mL ⋅ min-1 ⋅ kg-1 and HbA1c decreased by 0.73 and 0.85%, respectively. Univariate correlations confirmed these relationships, and mean changes in both MPVA and SED-time predicted changes in physical fitness and cardiovascular risk factors/scores independently of one another and of other confounders. Similar findings were observed with LPA and PA volume and in each group separately.

CONCLUSIONS

Even modest increments in MVPA may have a clinically meaningful impact, and reallocating SED-time to LPA may also contribute to improved outcomes, possibly by increasing total energy expenditure.