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Long-term Weight Loss in a 24 Month Primary Care-Anchored Telehealth Lifestyle Coaching Program: Randomized Controlled Trial.
Abstract
Long-term weight loss can reduce the risk of type 2 diabetes for people living with obesity and reduce complications for patients diagnosed with type 2 diabetes. We investigated whether a telehealth lifestyle-coaching program (Liva) leads to long-term (24 months) weight loss compared to usual care. In a randomized controlled trial, n = 340 participants living with obesity with or without type 2 diabetes were enrolled and randomized via an automated computer algorithm to an intervention group ( n = 200) or to a control group ( n = 140). The telehealth lifestyle-coaching program comprised of an initial one-hour face-to-face motivational interview followed by asynchronous telehealth coaching. The behavioural change techniques used were enabled by individual live monitoring. The primary outcome was a change in body weight from baseline to 24 months. Data were assessed for n = 136 participants (40%), n = 81 from the intervention group and n = 55 from the control group, who completed the 24-month follow-up. After 24 months mean body weight and body mass index were reduced significantly for completers in both groups, but almost twice as much was registered for those in the intervention group which was not significant between groups −4.4 (CI −6.1; −2.8) kg versus −2.5 (CI −3.9; −1.1) kg, P = 0.101. Haemoglobin A1c was significantly reduced in the intervention group −3.1 (CI −5.0; −1.2) mmol/mol, but not in the control group −0.2 (CI −2.4; −2.0) mmol/mol without a significant between group difference ( P = 0.223). Low completion was partly due to coronavirus disease 2019. Telehealth lifestyle coaching improve long-term weight loss (> 24 months) for obese people with and without type 2 diabetes compared to usual care.
... La pre-obesidad y la obesidad por sí solas se asocian con una mayor mortalidad, evitando así la creencia de que el exceso de grasa corporal en sujetos sanos puede desempeñar un papel metabólicamente protector. (7) Para las personas que viven con obesidad, la pérdida de peso puede reducir el riesgo de enfermedades cardiovasculares, diabetes tipo 2 y diversas otras enfermedades no transmisibles, sin embargo, pueden reducir las complicaciones cuando los pacientes han desarrollado diabetes tipo 2. (8) Tradicionalmente, las intervenciones conductuales para la pérdida de peso tienen como objetivo aumentar la actividad física, mejorar la calidad de la dieta y reducir la ingesta total de calorías para promover la disminución del peso. (5) Se ha demostrado que la actividad física rutinaria mejora la composición corporal a través de la reducción de la adiposidad abdominal y un mejor control del peso, también mejora los perfiles de lipoproteínas lipídicas a través de la reducción de los niveles de triglicéridos, del aumento de los niveles de colesterol de lipoproteínas de alta densidad (HDL) y disminución de la proporción entre lipoproteínas de baja densidad (LDL) y alta densidad (HDL). ...
... La hemoglobina A1c se redujo significativamente en el grupo de intervención −3,1 mmol/mol, pero no en el grupo de control −0,2 mmol/mol. (8) Hesseldal L et al estudió 338 participantes con diagnóstico de obesidad con o sin diabetes tipo 2 asignados en un programa de entrenamiento de estilo de vida de eHealth, evaluando si resultaría en una pérdida de peso significativa a largo plazo (12 meses) en comparación con la atención habitual. Los participantes tenían un peso promedio de 103,7 kg, un IMC medio de 35,3 kg/m2, edad 18-70 años y HBA1C medio de 6,6 %. ...
... Alencar M et al, Christensen JR et al, Hesseldal L et al, Johnson KE et al, encontraron resultados significativos donde se demuestra que el uso combinado de videoconferencia junto con dispositivos m-health y la necesidad de una relación empática entre la persona que vive con obesidad y el profesional de la salud que ofrece el asesoramiento es necesaria y beneficiosa para el descenso de peso. (8,11,16,18) Christensen JR et al concluyeron que la tasa de abandono al estudio aumentó al final del estudio debido a la comunicación poco frecuente entre el participante y el profesional de la salud que no pudo mantener la relación empática. (8) Johnson KE et al demostraron que la falta de asesoramiento virtual al grupo presencial podría ser una de las razones por la que hubo diferencias en la pérdida de peso comparada con el grupo virtual. ...
Background: obesity is a multifactorial disease with high growth rates currently being considered an epidemic of the 21st century. The health area has adapted to the use of technology, using different applications focused on changing habits in patients with the intention of achieving weight reduction in them. Method: a systematic review was carried out in different online engines such as PubMed, Cochrane Library and Google Scholar. Results: were selected 8 randomized clinical trials after applying the inclusion and exclusion criteria that sought to demonstrate the benefits of using new techniques such as telemedicine in people with obesity to provide a better quality of life. Conclusion: in obese patients who used telemedicine as part of the treatment, a greater weight reduction was observed compared to patients who did not use it.
... In healthcare, translating research evidence into practical applications remains a critical challenge, particularly when implementing evidence-based guidelines and interventions. 1 The fidelity assessment of these interventions, especially in behaviour change (BC) programmes, faces challenges due to the heterogeneity in methodological approaches. 2 BC interventions play a pivotal role in addressing diverse health issues, including chronic diseases, mental health disorders, and lifestyle modifications, 3,4 and their successful implementation on a large scale has profound implications for public health. 5 An accurate description of BC programmes serves to identify crucial intervention components and expand theory and ensures their applicability across various healthcare settings and populations. ...
... Health coaching is an effective and scalable practice for supporting BC. 4,17 Despite the identification of the theoretical frameworks and techniques employed, the replication and implementation of effective coaching interventions faces challenges. Accurate descriptions of how the intervention is delivered, including the strategies, language, and delivery styles, at all intervention stages, as well as how these are perceived by the patient, is essential. ...
Introduction
Digital health coaching interventions for behaviour change (BC) are effective in addressing various health conditions. Implementing these requires accurate descriptions of components and health coaches (HC) delivery methods, alongside understanding patients’ perceptions of these interactions. The HC–patient relationship significantly influences BC outcomes. Here, empathy is an important driver that enables HCs to offer tailored advice that resonates with patients’ needs, fostering motivation. Yet, defining and measuring empathy remains a challenge. In this study, we draw on various BC frameworks and Pounds’ empathy appraisal approach to categorise HCs responses to patient cues and explore the interplay between empathy and BC.
Methods
Using a two-round survey, we collected responses from 11 HCs to 10 patient messages from the Bump2Baby and Me trial in a simulated interaction. We analysed 88 messages to identify empathic responses and behaviour change techniques.
Results
Patients’ implicit empathy opportunities showed higher response rates than explicit ones. HCs prioritised positive reinforcement and employed various strategies to achieve similar objectives. The most common empathic response was ‘Acceptance’ for patients’ implicit positive expressions of self-judgement. HCs emphasised relatedness-support and competence-promoting techniques for implicit negative feelings and judgements, such as ‘Show unconditional regard’ and ‘Review behaviour goals’, and ‘Action planning and Problem-solving’ techniques to address explicit negative appreciations and feelings.
Conclusion
The use of different techniques with the same objective highlights the complexity of BC interactions. Further research is needed to explore the impact of this variability on patient outcomes and programme fidelity.
... The weight-loss rates using the Diabetes Prevention Program (DPP) via videoconferencing were similar to those with in-person visits [1]. Telehealth lifestyle coaching has been shown to improve long-term weight loss for people with obesity and T2DM compared to usual care [2,3]. A recent study suggested that the virtual format of the Why WAIT program was feasible and potentially as successful as the in-person program in a small pilot study [4]. ...
... Although the mentioned study reinforces the observation of similar weight loss between telehealth and on-site interventions, it did not focus on patients with obesity and medically treated diabetes, did not report glycaemic control and diabetes improvement, and achieved less than half the weight loss that was achieved in this study. Christensen et all, also reported a similar weight loss and HbA1c reduction among patients who received telehealth lifestyle coaching and usual care for 24 months [2]. Although in contrast to the present study's protocol, the control group in Christensen et al did not receive the same care as the telehealth group, and the weight loss (3.9 kg vs. 12.6 kg) and HbA1c reduction (0.4% vs. 1.3%) were lower compared to that in this study at 6 months. ...
Aims
Telehealth became a patient necessity during the COVID pandemic and evolved into a patient preference in the post‐COVID era. This study compared the % total body weight loss (%TBWL), HbA1c reduction, and resource utilization among patients with obesity and diabetes who participated in lifestyle interventions with or without telehealth.
Methods
A total of 150 patients with obesity and diabetes who were followed every 4–6 weeks either in‐person ( n = 83) or via telehealth ( n = 67), were included. All patients were provided with an individualized nutritional plan that included a weight‐based daily protein intake from protein supplements and food, an activity/sleep schedule‐based meal times, and an aerobic exercise goal of a 2000‐calorie burn/week, customized to patient's preferences, physical abilities, and comorbidities. The goal was to lose 10%TBWL. Telehealth‐based follow‐up required transmission via texting of weekly body composition measurements and any blood glucose levels below 100 mg/dl for medication adjustments. Weight, BMI, %TBWL, HbA1c (%), and medication effect score (MES) were compared. Patient no‐show rates, number of visits, program duration, and drop‐out rate were used to assess resource utilization based on cumulative staff and provider time spent (CSPTS), provider lost time (PLT) and patient spent time (PST).
Results
Mean age was 47.2 ± 10.6 years and 74.6% were women. Mean Body Mass Index (BMI) decreased from 44.1 ± 7.7–39.7 ± 6.7 kg/m ² ( p < 0.0001). Mean program duration was 189.4 ± 169.3 days. An HbA1c% unit decline of 1.3 ± 1.5 was achieved with a 10.1 ± 5.1%TBWL. Diabetes was cured in 16% (24/150) of patients. %TBWL was similar in regards to telehealth or in‐person appointments (10.6% ± 5.1 vs. 9.6% ± 4.9, p = 0.14). Age, initial BMI, MES, %TBWL, and baseline HbA1c had a significant independent effect on HbA1c reduction ( p < 0.0001). Program duration was longer for in‐person follow‐up (213.8 ± 194 vs. 159.3 ± 127, p = 0.019). The mean annual telehealth and in‐person no‐show rates were 2.7% and 11.2%, respectively ( p < 0.0001). Mean number of visits (5.7 ± 3.0 vs. 8.6 ± 5.1) and drop‐out rates (16.49% vs. 25.83%) were lower in telehealth group ( p < 0.0001). The CSPTS (440.4 ± 267.5 min vs. 200.6 ± 110.8 min), PLT (28.9 ± 17.5 min vs. 3.1 ± 1.6 min), and PST (1033 ± 628 min vs. 113.7 ± 61.4 min) were significantly longer ( p < 0.0001) for the in‐person group.
Conclusions
Telehealth offered comparable %TBWL and HbA1c decline as in‐person follow‐up, but with a shorter follow‐up, fewer appointments, and no‐shows. If improved resource utilization is validated by other studies, telehealth should become the standard of care for the management of obesity and diabetes.
... The percentage of individuals attaining clinically significant long-term weight reduction can be increased, and weight loss maintenance can be improved by providing extended care through individual telephone counselling and telehealth programmes (18,19). Even with the help of technology, adherence to dietary and nutritional therapy frequently declines with time. ...
One in eight people globally, prevalent mainly in some ethnic groups and those from low socioeconomic backgrounds, is a critical global health challenge of obesity. The telehealth system would be valuable in arresting obesity, with better access to care and a supporting digital community that encourages regular practice of physical activities, healthy diets, and health monitoring, all within reach and at affordable prices. Digital health interventions such as telehealth, mHealth applications, and wearable devices are new modalities for the treatment of obesity that increase monitoring of energy expenditure, physical activity level, and caloric intake. These technologies enhance the possibilities of therapy against barriers, such as maintaining motivation and improving the diet. This review summarises current evidence regarding digital health interventions for obesity management by considering and evaluating various global digital strategies to reduce obesity. The literature emphasises the effectiveness of eHealth interventions toward weight loss and maintenance. Furthermore, artificial intelligence (AI) and Information and Communications Technology (ICT) can predict and prevent paediatric obesity. By contrast, virtual reality (VR) applications can determine real-world behaviour in clinical practice. These digital interventions could increase the reach and efficacy of traditional weight management programmes by becoming more embedded in clinical practice. However, because of their broad implementation across different clinical settings, concerns regarding the security and privacy of these technologies must be addressed.
... 28 A randomized controlled trial (RCT) confirmed that telehealth coaching improves long-term weight loss for obese people, in comparison to usual care. 29 A recent literature review categorized weight-loss RCTs by duration, identifying long-term programs as those extending beyond 12 months and short-term programs as those lasting up to 6 months. Variations in program duration significantly impacted outcomes, particularly in modifying chronic disease risk factors, which are more likely to show improvement in longterm interventions. ...
Background: Ensuring patients’ responsiveness to, acceptance of, and adherence to any telehealth service is essential before its implementation.
Objectives: The aim of this pilot study was to assess patients’ responsiveness toward telemonitoring and telehealth coaching within a telenutrition weight-loss program.
Study Design: Twenty-five overweight and obese participants were enrolled in a weight-loss program that was supported by weekly telemonitoring and monthly telehealth coaching sessions. The response rate of participants to telemonitoring and their attendance rate at the telehealth coaching sessions were measured throughout a 6-month period (totaling 36 weeks), in addition to the examination of correlations and trends in both health and quality of life measures.
Results: The findings showed that participants’ response rate (%) for telemonitoring was at its highest rate (92%) in week 1, but this decreased dramatically and reached 19% in week 36. Meanwhile, 88% of the participants attended the first telehealth coaching session, followed by a slight decrease in the attendance rate (%) to 64% at the last session. The data indicate significant correlations between weight loss and improvements in Circle of Life (CoL) satisfaction rates, particularly between creativity, fun, and social life, confirming the positive benefits of integrative approaches to weight management. Most importantly, the data confirmed that attending more telehealth coaching sessions had a significant effect on weight loss, physical activity (measured in steps), and satisfaction with CoL aspects such as creativity, education, fun, social life, and spirituality.
Conclusions: Thus, our findings highlight that participants were more committed and responsive to telehealth coaching than telemonitoring during long-duration weight management interventions. Thus, future studies should consider exploring the potential of telehealth devices to enhance patients’ responsiveness to telemonitoring.
... Meaningfulness for patients of combined digital platform and structured telephone support [28] Number of hospital admissions [45] Average minutes per day of PA per patient [56] Usage of the online self-management platform [59] Self-evaluated, healthrelated quality of life [63] Diabetes Mellitus Reduction in HbA1c≥6 mmol/mol (0.5%) at 12 months [26] Weight loss from baseline at 24 months [32] Mean body weight [33] Changes in physical and mental health status [76] Changes in HbA1c [39] HbA1c at 6 and 12 months [48] Change in HbA1c levels from baseline to 24 months [50] Self-reported diabetes self-care [58] Change from baseline of HbA1c at 1 year [73] Change in HbA1c level after 1 year [74] HbA1c at 12 months [75] Usage of the e-Vita online care platform [36] Self-evaluated, healthrelated quality of life [63] Chronic Lung Disease General self-efficacy [27] General self-management [60] Changes in physical and mental health status [76] N of deaths [47] Meaningfulness for patients of combined digital platform and structured telephone support [28] N of hospital admissions [45] N of hospital admissions [47] Average minutes per day of PA per patient [56] Use of web portal [70] ...
... 38 Roczen is also comparable to other digitally delivered lifestyle interventions, where care is delivered by dietitians or health coaches via digital applications rather than clinicians. 39,40 Our retention rates was comparable to those in other Tier 3 specialist obesity program, with drop out ranging from 10% to 78% at the end of follow-up. 41 Overall, this service evaluation shows encouraging outcomes when compared to well established in-person and digitally delivered NHS program. ...
Introduction
The health of the United Kingdom workforce is key; approximately 186 million days are lost to sickness each year. Obesity and type 2 diabetes (T2D) remain major global health challenges. The aim of this retrospective service evaluation was to assess the impact of a digitally enabled, time‐restricted eating (TRE) intervention (Roczen Program, Reset Health Ltd) on weight and other health‐related outcomes.
Methods
This service evaluation was conducted in people living with overweight/obesity, with 89% referred from public sector employers. Participants were placed on a TRE, low‐carbohydrate, moderate protein plan delivered by clinicians and mentors with regular follow up, dietary guidance, goal setting, feedback, and social support.
Results
A total of 660 members enrolled and retention was 41% at 12 months. The majority were female (73.2%), 58.9% were of White ethnicity, with a mean (SD) age of 47.5 years (10.1), and a body mass index of 35.0 kg/m² (5.7). Data were available for 82 members at 12‐month. At 12‐month, members mean actual and percentage weight loss was −9.0 kg (7.0; p < 0.001) and −9.2% (6.7, p < 0.001) respectively and waist circumference reduced by −10.3 cm (10.7 p < 0.001), with 45.1% of members achieving ≥10% weight loss. Glycated hemoglobin was significantly improved at 6 months in people living with T2D (−11 mmol/mol [5.7] p = 0.012). Binge eating score significantly reduced (−4.4 [7.0] p = 0.006), despite cognitive restraint increasing (0.37 [0.6] p = 0.006).
Conclusion
Our service evaluation showed that the Roczen program led to clinically meaningful improvements in body weight, health‐related outcomes and eating behaviors that were sustained at 12‐month.
... 15 Digital tools for weight reduction seem tempting, but a Danish study found that the most important success factor for weight reduction was an empathic and trusting relationship between the patient and GP. 16 Another study confirms that patients prefer to receive advice about weight reduction from their GP. 17 Considerable initial weight reduction 18 and close follow-up, particularly in the early phase, 19 may be significant factors. ...
Overweight and obesity are among the most serious health problems of our time.
A majority of patients with overweight and obesity will first get in touch with health services through primary care. This makes it crucial to develop strategies to enable physicians in primary care to help and treat patients with overweight and obesity. The physicians tend to avoid this subject. The main reason is reported to be lack of knowledge and education, and that they have nothing concrete to offer their patients.
We wanted to examine if a simple method with specific measures could be used in Norwegian general practice and achieve meaningful weight loss.
23 physicians and 210 patients participated in the study.
The physicians who participated were cluster randomised into either control group or intervention group. The physicians in the control group were told to follow their usual approach, while the physicians in the intervention group followed a fixed plan with specific diets given orally and in writing to the patients. The inclusion criteria for both groups were: body mass index (BMI)>30 kg/m ² , or BMI>25 kg/m ² with at least one weight-related condition. Weight was measured at the start, then after 1 year and finally after 2 years in both groups.
We found no significant weight loss in the control group. In the intervention group, there was a weight loss of at least 10% by 25.5% after the first year and 24.2% after the entire observation period. 53.5% of the patients lost at least 5% of their weight in the first year and nearly 45% after the entire observation period. We conclude that a simple tool with a specific diet and activity plan is feasible in general practice and may produce significant weight loss. Trial registration number: NCT03000062 .
... Studies show that teleconsultation with a nutritionist is a preponderant tool in stimulating changes in eating habits, reaching goals, and overcoming barriers to achieving a healthy diet, as well as increasing patient satisfaction [24][25][26]. In addition, it is important to point out that the empathetic way in which follow-up care is given is a relevant and beneficial aspect that contributes to the patient's change [27,28]. Although the consultation is performed virtually, the way the health professional welcomes and guides the assisted individual helps to create bonds and increases patient satisfaction in the telehealth setting [29]. ...
... Since an unhealthy lifestyle is a leading cause of the development of T2DM, studies have shown that within the first years after diabetes diagnosis structured, non-pharmacological lifestyle interventions can significantly improve metabolic control [4][5][6], up to glycated haemoglobin (HbA 1c ) normalisation [7] and diabetes remission [8,9]. In this context, telemedical support programs were developed, which differed, for example, in the technical implementation, type of contact, or target parameters. ...
The effectiveness of the multimodal Telemedical Lifestyle Intervention Program (TeLIPro) was proven in the advanced stages of type 2 diabetes mellitus (T2DM). Since its therapeutic potential focusing on telemedical coaching without using a formula diet is unknown, we evaluated improvements in HbA1c, HbA1c normalisation rate, cardiometabolic risk factors, quality-of-life, and eating behaviour in real life. In this randomized-controlled trial, AOK Rhineland/Hamburg insured T2DM patients (n = 1163) were randomized (1:1) into two parallel groups, and 817 received the allocated intervention. In addition to routine care, all participants got scales, step counters, and access to an online portal. The TeLIPro group additionally received equipment for self-monitoring of blood glucose and telemedical coaching. Data were collected at baseline, after 6 and 12 months of intervention as well as after a 6-month follow-up. The primary endpoint after 12 months was (i) the estimated treatment difference (ETD) in HbA1c change and (ii) the HbA1c normalisation rate in those with diabetes duration < 5 years. The TeLIPro group demonstrated significantly stronger improvements in HbA1c (ETD −0.4% (−0.5; −0.2); p < 0.001), body weight, body-mass-index, quality-of-life, and eating behaviour, especially in T2DM patients with diabetes duration ≥ 5 years (ETD −0.5% (−0.7; −0.3); p < 0.001). The HbA1c normalisation rate did not significantly differ between groups (25% vs. 18%). Continuous addition of TeLIPro to routine care is effective in improving HbA1c and health-related lifestyle in T2DM patients with longer diabetes duration in real life.
... Since an unhealthy lifestyle is a leading cause of the development of T2DM, studies have shown that within the first years after diabetes diagnosis structured, non-pharmacological lifestyle interventions can significantly improve metabolic control [4][5][6], up to glycated haemoglobin (HbA 1c ) normalisation [7] and diabetes remission [8,9]. In this context, telemedical support programs were developed, which differed, for example, in the technical implementation, type of contact, or target parameters. ...
... Recruiting participants through general practitioners could be a potential strategy to avoid a selected study population of individuals with Type 2 diabetes. Also, e-health and m-health strategies for weight loss have proven relevant and feasible in this context [40], supporting the relevance to recruit from general practitioners. ...
Current physical activity interventions for individuals with Type 2 diabetes do not accommodate the needs of the individual in terms of content, time, and location. The aim of this study was to evaluate the feasibility and acceptability of an 8-week high intensity online physical exercise intervention combined with online group meetings and supported by an activity watch in individuals with Type 2 diabetes. This study was designed as a one-armed feasibility study and the intervention was developed using a co-creation approach. A total of 19 individuals with Type 2 diabetes participated in eight weeks of 30 min online physical exercise intervention followed by 30 min online group meetings in smaller groups once a week. Outcomes included pre-defined research progression criteria, secondary measurements of health parameters, and participant feedback. Most research progression criteria reached a level of acceptance, with the exception of participant recruitment, burden of objectively measured physical activity, and adverse events, where changes are needed before continuing to an RCT. Combining online physical exercise with online group meetings supported by an activity watch is feasible and acceptable in individuals with Type 2 diabetes with a higher educational level compared to the general population with Type 2 diabetes.
Aim
To assess the cost‐effectiveness of a digital diabetes prevention programme (d‐DPP) compared with a diabetes prevention programme (DPP) for preventing type 2 diabetes (T2D) in individuals with prediabetes in the United States.
Methods
A Markov cohort model was constructed, simulating a 10‐year period starting at the age of 45 years, with a societal and healthcare sector perspective. The effectiveness of the d‐DPP intervention was evaluated using a meta‐analysis, with that of the DPP as the comparator. The initial cycle represented the treatment period, and transition probabilities for the post‐treatment period were derived from a long‐term lifestyle intervention meta‐analysis. The onset of T2D complications was estimated using microsimulation. Quality‐adjusted life years (QALYs) were calculated based on health utility measured by short form (SF)‐12 scores, and a willingness‐to‐pay threshold of 3,672 from a societal perspective and $2,990 from a healthcare sector perspective and a gain of 0.08 QALYs compared with the DPP. The dropout rate was identified as a significant factor influencing the results. Probabilistic sensitivity analysis showed that the d‐DPP intervention was preferred in 85.8% in the societal perspective and 85.2% in the healthcare sector perspective.
Conclusions
The d‐DPP is a cost‐effective alternative to in‐person lifestyle interventions for preventing the development of T2D among individuals with prediabetes in the United States.
Background
Digital health interventions show promise for weight management. However, few text-based behavior change interventions have been designed to support patients receiving intragastric balloons, and none have simultaneously evaluated weight loss, psychological well-being, and behavior change despite the crucial interplay of these factors in weight management.
Objective
This study aims to assess whether a health coach–led, asynchronous, text-based digital behavior change coaching intervention (DBCCI) delivered to participants receiving an intragastric balloon and its aftercare program was feasible and acceptable to participants and supported improved outcomes, including weight loss, psychological well-being, and lifestyle behavior change conducive to weight loss maintenance.
Methods
This 12-month, single-arm prospective study enrolled adults aged 21 to 65 years with BMI ≥27 kg/m2 receiving a procedureless intragastric balloon (PIGB) at 5 bariatric clinics in the United Kingdom and the Netherlands. Participants received the DBCCI and the clinic-led PIGB aftercare program (remotely delivered) for 6 months after PIGB placement and then no intervention for an additional 6 months. The DBCCI was an evidence-based, personalized intervention wherein health coaches supported participants via exchanged asynchronous in-app text-based messages. Over the 12-month study, we assessed percentage of total body weight loss and psychological well-being via self-administered validated questionnaires (Warwick-Edinburgh Mental Wellbeing Scale, Generalized Anxiety Disorder Scale, Impact of Weight on Quality of Life–Lite–Clinical Trials Version, Loss of Control Over Eating Scale–Brief, Weight Efficacy Lifestyle Questionnaire–Short Form, and Barriers to Being Active Quiz). Participant engagement with and acceptability of the intervention were assessed via self-reported surveys.
Results
Overall, 107 participants (n=96, 89.7% female; mean baseline BMI 35.4, SD 5.4 kg/m2) were included in the analysis. Mean total body weight loss was 13.5% (SEM 2.3%) at the end of the DBCCI and 11.22% (SEM 2.3%) at the 12-month follow-up (P<.001). Improvements were observed for all psychological well-being measures throughout the 12 months except for the Generalized Anxiety Disorder Scale (improvement at month 1) and Barriers to Being Active Quiz (improvements at months 3 and 6). Surveys showed high levels of engagement with and acceptability of the DBCCI.
Conclusions
This study provides evidence that the health coach–led, asynchronous, text-based DBCCI was engaging and acceptable to participants with overweight and obesity. The DBCCI, delivered alongside the PIGB and its aftercare program, supported improved weight loss outcomes and psychological well-being versus baseline and was associated with lifestyle behavior changes known to help achieve and maintain long-term weight loss and improved health outcomes. Follow-up findings suggest a potential need for longer-term, more intense coaching to focus on weight loss maintenance and support ongoing self-coaching. This could be achieved by leveraging generative artificial intelligence to provide ongoing automated behavior change coaching support to augment human-led care.
Trial Registration
ClinicalTrials.gov NCT05884606; https://clinicaltrials.gov/study/NCT05884606
Objectives:
To assess the cost-effectiveness of a digital Diabetes Prevention Program (dDPP) in preventing type 2 diabetes mellitus among prediabetic patients from a health system perspective over a 10-year time horizon.
Methods:
A Markov cohort model was constructed to assess the cost-effectiveness of dDPP compared to a small group education (SGE) intervention. Transition probabilities for the first year of the model were derived from two clinical trials on dDPP. Transition probabilities for longer-term effects were derived from meta-analyses on lifestyle and Diabetes Prevention Program interventions. Cost and health utilities were derived from published literature. Partial completion of interventions was incorporated to provide a robust prediction of a real-world deployment. Parameter uncertainties were assessed using univariate and probabilistic sensitivity analyses. Cost-effectiveness was measured by an incremental cost-effectiveness ratio (ICER) between dDPP and SGE from a health system perspective over a 10-year time horizon.
Results:
The dDPP dominated the SGE at the 50,000, 100,000, and 100,000 willingness-to-pay threshold (WTP) revealed a dominated ICER, with the SGE costing 100,000 WTP thresholds.
Conclusions:
The findings comparing a dDPP to an SGE suggest that a dDPP can be cost-effective for patients with a high risk of developing type 2 diabetes.
The goal of this trial was to investigate whether an eHealth lifestyle coaching programme led to significant weight loss and decreased Haemoglobin A1c (HbA1c) in patients with type 2 diabetes. In an RCT, 170 patients were enrolled from 2018 to 2019 for intervention or control. Inclusion criteria were diagnosed with type 2 diabetes, BMI 30–45 kg/m2, and aged 18–70 years. Exclusion criteria were lacks internet access, pregnant or planning a pregnancy, or has a serious disease. Primary and secondary outcomes were a reduction in body weight and HbA1c. At six months, 75 (75%) patients in the intervention group and 53 (76%) patients in the control group remained in the trial. The mean body weight loss was 4.2 kg (95% CI, −5.49; −2.98) in the intervention group and 1.5 kg (95% CI, −2.57; −0.48) in the control group (p = 0.005). In the intervention group, 24 out of 62 patients with elevated HbA1c at baseline (39%) had a normalized HbA1c < 6.5% at six months, compared to 8 out of 40 patients with elevated HbA1c at baseline (20%) in the control group (p = 0.047). The eHealth lifestyle coaching programme can lead to significant weight loss and decreased HbA1c among patients with type 2 diabetes, compared to standard care.
Background:
Long-term weight loss in people living with obesity can reduce the risk and progression of noncommunicable diseases. Observational studies suggest that digital coaching can lead to long-term weight loss.
Objective:
We investigated whether an eHealth lifestyle coaching program for people living with obesity with or without type 2 diabetes led to significant, long-term (12-month) weight loss compared to usual care.
Methods:
In a randomized controlled trial that took place in 50 municipalities in Denmark, 340 people living with obesity with or without type 2 diabetes were enrolled from April 16, 2018, to April 1, 2019, and randomized via an automated computer algorithm to an intervention (n=200) or a control (n=140) group. Patients were recruited via their general practitioners, the Danish diabetes organization, and social media. The digital coaching intervention consisted of an initial 1-hour face-to-face motivational interview followed by digital coaching using behavioral change techniques enabled by individual live monitoring. The primary outcome was change in body weight from baseline to 12 months.
Results:
Data were assessed for 200 participants, including 127 from the intervention group and 73 from the control group, who completed 12 months of follow-up. After 12 months, mean body weight and BMI were significantly reduced in both groups but significantly more so in the intervention group than the control group (-4.5 kg, 95% CI -5.6 to -3.4 vs -1.5 kg, 95% CI -2.7 to -0.2, respectively; P<.001; and -1.5 kg/m2, 95% CI -1.9 to -1.2 vs -0.5 kg/m2, 95% CI -0.9 to -0.1, respectively; P<.001). Hemoglobin A1c was significantly reduced in both the intervention (-6.0 mmol/mol, 95% CI -7.7 to -4.3) and control (-4.9 mmol/mol, 95% CI -7.4 to -2.4) groups, without a significant group difference (all P>.46).
Conclusions:
Compared to usual care, digital lifestyle coaching can induce significant weight loss for people living with obesity, both with and without type 2 diabetes, after 12 months.
Trial registration:
ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915.
Background
Mobile health (mHealth) apps are considered to be potentially powerful tools for improving lifestyles and preventing cardiovascular disease (CVD), although only few have undergone large, well-designed epidemiological research. “kencom” is a novel mHealth app with integrated functions for healthy lifestyles such as monitoring daily health/step data, providing tailored health information, or facilitating physical activity through group-based game events. The app is linked to large-scale Japanese insurance claims databases and annual health check-up databases, thus comprising a large longitudinal cohort.
Objective
We aimed to assess the effects of kencom on physical activity levels and CVD risk factors such as obesity, hypertension, dyslipidemia, and diabetes mellitus in a large population in Japan.
Methods
Daily step count, annual health check-up data, and insurance claim data of the kencom users were integrated within the kencom system. Step analysis was conducted by comparing the 1-year average daily step count before and after kencom registration. In the CVD risk analysis, changes in CVD biomarkers following kencom registration were evaluated among the users grouped into the quintile according to their change in step count.
ResultsA total of 12,602 kencom users were included for the step analysis and 5473 for the CVD risk analysis. The participants were generally healthy and their mean age was 44.1 (SD 10.2) years. The daily step count significantly increased following kencom registration by a mean of 510 steps/day (P
BACKGROUND
Telemedicine is defined as the delivery of health services via remote communication and technology. It is a convenient and cost-effective method of intervention, which has shown to be successful in improving glyceamic control for type 2 diabetes patients. The utility of a successful diabetes intervention is vital to reduce disease complications, hospital admissions and associated economic costs.
AIM
To evaluate the effects of telemedicine interventions on hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), post-prandial glucose (PPG), fasting plasma glucose (FPG), weight, cholesterol, mental and physical quality of life (QoL) in patients with type 2 diabetes. The secondary aim of this study is to determine the effect of the following subgroups on HbA1c post-telemedicine intervention; telemedicine characteristics, patient characteristics and self-care outcomes.
METHODS
PubMed Central, Cochrane Library, Embase and Scopus databases were searched from inception until 18th of June 2020. The quality of the 43 included studies were assessed using the PEDro scale, and the random effects model was used to estimate outcomes and I2 for heterogeneity testing. The mean difference and standard deviation data were extracted for analysis.
RESULTS
We found a significant reduction in HbA1c [-0.486%; 95% confidence interval (CI) -0.561 to -0.410, P < 0.001], DBP (-0.875 mmHg; 95%CI -1.429 to -0.321, P < 0.01), PPG (-1.458 mmol/L; 95%CI -2.648 to -0.268, P < 0.01), FPG (-0.577 mmol/L; 95%CI -0.710 to -0.443, P < 0.001), weight (-0.243 kg; 95%CI -0.442 to -0.045, P < 0.05), BMI (-0.304; 95%CI -0.563 to -0.045, P < 0.05), mental QoL (2.210; 95%CI 0.053 to 4.367, P < 0.05) and physical QoL (-1.312; 95%CI 0.545 to 2.080, P < 0.001) for patients following telemedicine interventions in comparison to control groups. The results of the meta-analysis did not show any significant reductions in SBP and cholesterol in the telemedicine interventions compared to the control groups. The telemedicine characteristic subgroup analysis revealed that clinical treatment models of intervention, as well as those involving telemonitoring, and those provided via modes of videoconference or interactive telephone had the greatest effect on HbA1c reduction. In addition, interventions delivered at a less than weekly frequency, as well as those given for a duration of 6 mo, and those lead by allied health resulted in better HbA1c outcomes. Furthermore, interventions with a focus on biomedical parameters, as well as those with an engagement level > 70% and those with a drop-out rate of 10%-19.9% showed greatest HbA1c reduction. The patient characteristics investigation reported that Hispanic patients with T2DM had a greater HbA1c reduction post telemedicine intervention. For self-care outcomes, telemedicine interventions that resulted in higher post-intervention glucose monitoring and self-efficacy were shown to have better HbA1c reduction.
Background
Diabetes mellitus (DM) is one of the world’s greatest health threats with rising prevalence. Global digitalization leads to new digital approaches in diabetes management, such as telemedical interventions. Telemedicine, which is the use of information and communication technologies, may provide medical services over spatial distances to improve clinical patient outcomes by increasing access to diabetes care and medical information.
Objective
This study aims to examine whether telemedical interventions effectively improve diabetes control using studies that pooled patients with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), and whether the benefits are greater in patients diagnosed with T2DM than in those diagnosed with T1DM. We analyzed the primary outcome glycated hemoglobin A1c (HbA1c) and the secondary outcomes fasting blood glucose (FBG), blood pressure (BP), body weight, BMI, quality of life (QoL), cost, and time saving.
Methods
Publications were systematically identified by searching Cochrane Library, MEDLINE via PubMed, Web of Science Core Collection, Embase, and CINAHL databases for studies published between January 2008 and April 2020, considering systematic reviews (SRs), meta-analyses (MAs), randomized controlled trials (RCTs), and clinical trials (CTs). Study quality was assessed using the A Measurement Tool to Assess Systematic Reviews, Effective Public Health Practice Project, and National Institute for Health and Care Excellence qualitative checklist. We organized the trials by communication technologies in real-time video or audio interventions, asynchronous interventions, and combined interventions (synchronous and asynchronous communication).
Results
From 1116 unique citations, we identified 31 eligible studies (n=15 high, n=14 moderate, n=1 weak, and n=1 critically low quality). We selected 21 SRs and MAs, 8 RCTs, 1 non-RCT, and 1 qualitative study. Of the 10 trials, 3 were categorized as real-time video, 1 as real-time video and audio, 4 as asynchronous, and 2 as combined intervention. Significant decline in HbA1c levels based on pooled T1DM and T2DM patients data ranged from −0.22% weighted mean difference (WMD; 95% CI −0.28 to −0.15; P<.001) to −0.64% mean difference (95% CI −1.01 to −0.26; P<.001). The intervention effect on lowering HbA1c values might be significantly smaller for patients with T1DM than for patients with T2DM. Evidence on the impact on BP, body weight, FBG, cost effectiveness, and time saving was smaller compared with HbA1c but indicated potential in some publications.
Conclusions
Telemedical interventions might be clinically effective in improving diabetes control overall, and they might significantly improve HbA1c concentrations. Patients with T2DM could benefit more than patients with T1DM regarding lowering HbA1c levels. Further studies with longer duration and larger cohorts are necessary.
Aims:
To observe and report population demography, comorbidities, risk factor levels and risk factor treatment in a sample of individuals treated for type 2 diabetes in primary care in Norway, Sweden and Denmark.
Methods:
Retrospective observational cohort using extraction of data from electronic medical records linked with national health care registries.
Results:
Sixty primary care clinics participated with annual cross-sectional data (2003 to 2015). In 2015 the sample consisted of 31,632 individuals. Mean age (64.5-66.8 years) and proportion of women (43-45%) were similar. The prevalence of cardiovascular disease in 2015 was 40.7%, 41.6% and 38.0% for Norway, Sweden and Denmark, respectively and 84% to 89% of patients were receiving a pharmacological anti-diabetic treatment. More Danish patients reached targets for HbA1c and LDL cholesterol, while more patients in Sweden and Denmark met the blood pressure target of <130/80 mmHg as compared to Norway.
Conclusions:
In three comparable public primary health care systems we found a high prevalence of cardiovascular disease and differences in risk factor treatment and attainment of risk factor goals. With recent guideline changes there is potential for further prevention of diabetes complications in primary care in the future.
Background
Delivering self-management support to people with type 2 diabetes mellitus is essential to reduce the health system burden and to empower people with the skills, knowledge, and confidence needed to take an active role in managing their own health.
Objective
This study aims to evaluate the adoption, use, and effectiveness of the My Diabetes Coach (MDC) program, an app-based interactive embodied conversational agent, Laura, designed to support diabetes self-management in the home setting over 12 months.
Methods
This randomized controlled trial evaluated both the implementation and effectiveness of the MDC program. Adults with type 2 diabetes in Australia were recruited and randomized to the intervention arm (MDC) or the control arm (usual care). Program use was tracked over 12 months. Coprimary outcomes included changes in glycated hemoglobin (HbA1c) and health-related quality of life (HRQoL). Data were assessed at baseline and at 6 and 12 months, and analyzed using linear mixed-effects regression models.
Results
A total of 187 adults with type 2 diabetes (mean 57 years, SD 10 years; 41.7% women) were recruited and randomly allocated to the intervention (n=93) and control (n=94) arms. MDC program users (92/93 participants) completed 1942 chats with Laura, averaging 243 min (SD 212) per person over 12 months. Compared with baseline, the mean estimated HbA1c decreased in both arms at 12 months (intervention: 0.33% and control: 0.20%), but the net differences between the two arms in change of HbA1c (−0.04%, 95% CI −0.45 to 0.36; P=.83) was not statistically significant. At 12 months, HRQoL utility scores improved in the intervention arm, compared with the control arm (between-arm difference: 0.04, 95% CI 0.00 to 0.07; P=.04).
Conclusions
The MDC program was successfully adopted and used by individuals with type 2 diabetes and significantly improved the users’ HRQoL. These findings suggest the potential for wider implementation of technology-enabled conversation-based programs for supporting diabetes self-management. Future studies should focus on strategies to maintain program usage and HbA1c improvement.
Trial Registration
Australia New Zealand Clinical Trials Registry (ACTRN) 12614001229662; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12614001229662
Importance
Lifestyle interventions for obesity produce reductions in body weight that can decrease risk for diabetes and cardiovascular disease but are limited by suboptimal maintenance of lost weight and inadequate dissemination in low-resource communities.
Objective
To evaluate the effectiveness of extended care programs for obesity management delivered remotely in rural communities through the US Cooperative Extension System.
Design, Setting, and Participants
This randomized clinical trial was conducted from October 21, 2013, to December 21, 2018, in Cooperative Extension Service offices of 14 counties in Florida. A total of 851 individuals were screened for participation; 220 individuals did not meet eligibility criteria, and 103 individuals declined to participate. Of 528 individuals who initiated a 4-month lifestyle intervention, 445 qualified for randomization. Data were analyzed from August 22 to October 21, 2019.
Interventions
Participants were randomly assigned to extended care delivered via individual or group telephone counseling or an education control program delivered via email. All participants received 18 modules with posttreatment recommendations for maintaining lost weight. In the telephone-based interventions, health coaches provided participants with 18 individual or group sessions focused on problem solving for obstacles to the maintenance of weight loss.
Main Outcomes and Measures
The primary outcome was change in body weight from the conclusion of initial intervention (month 4) to final follow-up (month 22). An additional outcome was the proportion of participants achieving at least 10% body weight reduction at follow-up.
Results
Among 445 participants (mean [SD] age, 55.4 [10.2] years; 368 [82.7%] women; 329 [73.9%] white), 149 participants (33.5%) were randomized to individual telephone counseling, 143 participants (32.1%) were randomized to group telephone counseling, and 153 participants (34.4%) were randomized to the email education control. Mean (SD) baseline weight was 99.9 (14.6) kg, and mean (SD) weight loss after the initial intervention was 8.3 (4.9) kg. Mean weight regains at follow-up were 2.3 (95% credible interval [CrI], 1.2-3.4) kg in the individual telephone counseling group, 2.8 (95% CrI, 1.4-4.2) kg for the group telephone counseling group, and 4.1 (95% CrI, 3.1-5.0) kg for the education control group, with a significantly smaller weight regain observed in the individual telephone counseling group vs control group (posterior probability >.99). A larger proportion of participants in the individual telephone counseling group achieved at least 10% weight reductions (31.5% [95% CrI, 24.1%-40.0%]) than in the control group (19.1% [95% CrI, 14.1%-24.9%]) (posterior probability >.99).
Conclusions and Relevance
This randomized clinical trial found that providing extended care for obesity management in rural communities via individual telephone counseling decreased weight regain and increased the proportion of participants who sustained clinically meaningful weight losses.
Trial Registration
ClinicalTrials.gov Identifier: NCT02054624
Background:
Obesity is linked to a number of chronic health conditions, such as type 2 diabetes, heart disease, and cancer, and weight loss interventions are often expensive. Recent systematic reviews concluded that app and web-based interventions can improve lifestyle behaviors and weight loss at a reasonable cost, but long-term sustainability needs to be demonstrated.
Objective:
This study protocol is for a 2-year randomized controlled trial that aims to evaluate the clinical and economic effects of a primary care, anchored, collaborative, electronic health (eHealth) lifestyle coaching program (long-term Lifestyle change InterVention and eHealth Application [LIVA] 2.0) in obese participants with and without type 2 diabetes. The program's primary outcome is weight loss. Its secondary outcome is the hemoglobin A1c (HbA1c) level, and its tertiary outcomes are retention rate, quality of life (QOL), and cost effectiveness. Analytically, the focus is on associations of participant characteristics with outcomes and sustainability.
Methods:
We conduct a multicenter trial with a 1-year intervention and 1-year retention. LIVA 2.0 is implemented in municipalities within administrative regions in Denmark, specifically eight municipalities located within the Region of Southern Denmark and two municipalities located within the Capital Region of Denmark. The participants are assessed at baseline and at 6-, 12-, and 24-month follow-ups. Individual data from the LIVA 2.0 platform are combined with clinical measurements, questionnaires, and participants' usage of municipality and health care services. The participants have a BMI ≥30 but ≤45 kg/m2, and 50% of the participants have type 2 diabetes. The participants are randomized in an approximately 60:40 manner, and based on sample size calculations on weight loss and intention-to-treat statistics, 200 participants are randomized to an intervention group and 140 are randomized to a control group. The control group is offered the conventional preventive program of the municipality, and it is compared to the intervention group, which follows the LIVA 2.0 in addition to the conventional preventive program.
Results:
The first baseline assessments have been carried out in March 2018, and the 2-year follow-up will be carried out between March 2020 and April 2021. The hypothesis is that the trial results will demonstrate decreased body weight and that the number of patients who show normalization of their HbA1c levels in the intervention group will be much higher than that in the control group. The participants in the intervention group are also expected to show a greater decrease in their use of glucose-lowering medication and a greater improvement in their QOL when compared with the control group. Operational costs are expected to be lower than standard care, and the intervention is expected to be cost-effective.
Conclusions:
This is the first time that an app and web-based eHealth lifestyle coaching program implemented in Danish municipalities will be clinically and economically evaluated. If the LIVA 2.0 eHealth lifestyle coaching program is proven to be effective, there is great potential for decreasing the rates of obesity, diabetes, and related chronic diseases.
Trial registration:
ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915.
International registered report identifier (irrid):
DERR1-10.2196/19172.
Background:
Although there are many wearable devices available to help people lose weight and decrease the rising prevalence of obesity, the effectiveness of these devices in long-term weight management has not been established.
Objective:
This study aimed to systematically review the literature on using wearable technology for long-term weight loss in overweight and obese adults.
Methods:
We searched the following databases: Medical Literature Analysis and Retrieval System Online, EMBASE, Compendex, ScienceDirect, Cochrane Central, and Scopus. The inclusion criteria were studies that took measurements for a period of ≥1 year (long-term) and had adult participants with a BMI >24. A total of 2 reviewers screened titles and abstracts and assessed the selected full-text papers for eligibility. The risk of bias assessment was performed using the following tools appropriate for different study types: the Cochrane risk of bias tool, Risk Of Bias In Nonrandomized Studies-of Interventions, A MeaSurement Tool to Assess systematic Reviews, and 6 questions to trigger critical thinking. The results of the studies have been provided in a narrative summary.
Results:
We included five intervention studies: four randomized controlled trials and one nonrandomized study. In addition, we used insights from six systematic reviews, four commentary papers, and a dissertation. The interventions delivered by wearable devices did not show a benefit over comparator interventions, but overweight and obese participants still lost weight over time. The included intervention studies were likely to suffer from bias. Significant variances in objectives, methods, and results of included studies prevented meta-analysis.
Conclusions:
This review showed some evidence that wearable devices can improve long-term physical activity and weight loss outcomes, but there was not enough evidence to show a benefit over the comparator methods. A major issue is the challenge of separating the effect of decreasing use of wearable devices over time from the effect of the wearable devices on the outcomes. Consistency in study methods is needed in future long-term studies on the use of wearable devices for weight loss.
Trial registration:
PROSPERO CRD42018096932; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=96932.
Background
Diabetes and obesity have become epidemics and costly chronic diseases. The impact of mobile health (mHealth) interventions on diabetes and obesity management is promising; however, studies showed varied results in the efficacy of mHealth interventions.
Objective
This review aimed to evaluate the effectiveness of mHealth interventions for diabetes and obesity treatment and management on the basis of evidence reported in reviews and meta-analyses and to provide recommendations for future interventions and research.
Methods
We systematically searched the PubMed, IEEE Xplore Digital Library, and Cochrane databases for systematic reviews published between January 1, 2005, and October 1, 2019. We analyzed 17 reviews, which assessed 55,604 original intervention studies, that met the inclusion criteria. Of those, 6 reviews were included in our meta-analysis.
Results
The reviews primarily focused on the use of mobile apps and text messaging and the self-monitoring and management function of mHealth programs in patients with diabetes and obesity. All reviews examined changes in biomarkers, and some reviews assessed treatment adherence (n=7) and health behaviors (n=9). Although the effectiveness of mHealth interventions varied widely by study, all reviews concluded that mHealth was a feasible option and had the potential for improving patient health when compared with standard care, especially for glycemic control (−0.3% to −0.5% greater reduction in hemoglobin A1c) and weight reduction (−1.0 kg to −2.4 kg body weight). Overall, the existing 6 meta-analysis studies showed pooled favorable effects of these mHealth interventions (−0.79, 95% CI −1.17 to −0.42; I2=90.5).
Conclusions
mHealth interventions are promising, but there is limited evidence about their effectiveness in glycemic control and weight reduction. Future research to develop evidence-based mHealth strategies should use valid measures and rigorous study designs. To enhance the effectiveness of mHealth interventions, future studies are warranted for the optimal formats and the frequency of contacting patients, better tailoring of messages, and enhancing usability, which places a greater emphasis on maintaining effectiveness over time.
Objective:
This meta-analysis aimed to assess the effect of mobile application (app) interventions on weight loss in patients with type 2 diabetes.
Methods:
Electronic databases were searched for randomized controlled trials examining the use of mobile app interventions with outcomes on weight loss evaluated by body weight or other measures such as BMI or waist circumference. A random-effects model was applied to obtain weight mean differences and 95% CIs.
Results:
Fourteen studies enrolling 2,129 patients with type 2 diabetes were included. Mobile app interventions could significantly reduce body weight (weight mean difference, -0.84 kg; 95% CI: -1.51 to -0.17 kg) and lower waist circumference (-1.35 cm; 95% CI: -2.16 to -0.55 cm) but may not decrease BMI (-0.08 kg/m2 ; 95% CI: -0.41 to 0.25 kg/m2 ). The reductions appeared to be more pronounced in patients with obesity or among studies using mobile app interventions combined with other behavior components. However, weight loss was not moderated by the functionalities of the mobile apps (all Pinteraction > 0.05) or by the intervention duration (all P > 0.87).
Conclusions:
Mobile app interventions lead to weight loss in patients with type 2 diabetes and are worth recommending for weight loss promotion.
Background
Half of the Danish population is overweight or obese. Obesity can negatively impact health and daily life. The Danish National Board of Health´s guidelines for weight loss programmes to the Danish municipalities, recommends multidisciplinary teams, including occupational therapy, and interventions targeting diet, exercise, psychosocial coping, and everyday life.
Aim
To describe the structure and content of obesity programmes offered by the 98 municipalities in Denmark, including details such as the health professionals, programme recipients, dose, structure, content, and the role of occupational therapists.
Method
A quantitative content analysis was conducted on 234 published Danish municipal weight loss programmes. Programme descriptions were identified through internet searches using both sundhed.dk and Google.com.
Results
Various health professionals conducted the programmes, and five involved occupational therapists. Programmes targeted children, adolescent and adults. Dose, structure and content were heterogeneous.
Conclusion
The majority of the programmes were neither evidence-based, nor did they follow recommendations from Danish National Board of Health. Few programmes addressed the role of habits or social participation. Occupational therapists appear to be under-utilized as providers of the programmes.
Significance
Occupational therapists have a role to play in weight loss programmes, because of their training in activity analysis and their consideration of people, environments, and occupations. These components are included in the recommendations about psychosocial aspects and everyday life from the Danish National Board of Health.
The rising burden of type 2 diabetes is a major concern in healthcare worldwide. This research aimed to analyze the global epidemiology of type 2 diabetes. We analyzed the incidence, prevalence, and burden of suffering of diabetes mellitus based on epidemiological data from the Global Burden of Disease (GBD) current dataset from the Institute of Health Metrics, Seattle. Global and regional trends from 1990 to 2017 of type 2 diabetes for all ages were compiled. Forecast estimates were obtained using the SPSS Time Series Modeler. In 2017, approximately 462 million individuals were affected by type 2 diabetes corresponding to 6.28% of the world’s population (4.4% of those aged 15–49 years, 15% of those aged 50–69, and 22% of those aged 70+), or a prevalence rate of 6059 cases per 100,000. Over 1 million deaths per year can be attributed to diabetes alone, making it the ninth leading cause of mortality. The burden of diabetes mellitus is rising globally, and at a much faster rate in developed regions, such as Western Europe. The gender distribution is equal, and the incidence peaks at around 55 years of age. Global prevalence of type 2 diabetes is projected to increase to 7079 individuals per 100,000 by 2030, reflecting a continued rise across all regions of the world. There are concerning trends of rising prevalence in lower-income countries. Urgent public health and clinical preventive measures are warranted.
Background:
Patients with type 2 diabetes mellitus (T2DM) receiving primary care regularly visit their practice nurses (PNs). By actively participating during medical consultations, patients can better manage their disease, improving clinical outcomes and their quality of life. However, many patients with T2DM do not actively participate during medical consultations. To understand the factors affecting engagement of patients with T2DM, this study aimed to identify factors that help or hinder them from actively participating in consultations with their primary care PNs.
Methods:
Two semi-structured focus groups and 12 semi-structured individual interviews were conducted with patients with T2DM (n = 20) who were undergoing treatment by primary care PNs. All interviews were transcribed verbatim and analyzed using a two-step approach derived from the context-mapping framework.
Results:
Four factors were found to help encourage patients to actively participate in their consultation: developing trusting relationships with their PNs, having enough time in the appointment, deliberately preparing for consultations, and allowing for the presence of a spouse. Conversely, four factors were found to hinder patients from participating during consultations: lacking the need or motivation to participate, readjusting to a new PN, forgetting to ask questions, and ineffectively expressing their thoughts.
Conclusion:
Patients lacked the skills necessary to adequately prepare for a consultation and achieve an active role. In addition, patients' keen involvement appeared to benefit from a trusting relationship with their PNs. When active participation is impeded by barriers such as a lack of patient's skills, facilitators should be introduced at an early stage.
Trial registration:
Current Controlled Trials NTR4693 (July 16, 2014).
Background
Internet and mobile interventions aiming to promote healthy lifestyle have attracted much attention because of their scalability and accessibility, low costs, privacy and user control, potential for use in real-life settings, as well as opportunities for real-time modifications and interactive advices. A real-life electronic health (eHealth) lifestyle coaching intervention was implemented in 8 Danish municipalities between summer 2016 and summer 2018.
Objective
The aim of this study was to assess the effects associated with the eHealth intervention among diabetes patients in a real-life municipal setting. The eHealth intervention is based on an initial meeting, establishing a strong empathic relationship, followed by digital lifestyle coaching and collaboration supported by a Web-based community among patients.
Methods
We conducted an observational study examining the effect of an eHealth intervention on self-reported weight change among 103 obese diabetes patients in a real-life municipal setting. The patients in the study participated in the eHealth intervention between 3 and 12 months. A weight change was observed at 6, 9, and 12 months. We used regression methods to estimate the impacts of the intervention on weight change.
Results
We found that the eHealth intervention significantly reduced weight among diabetes patients, on average 4.3% of the initial body mass, which corresponds to 4.8 kg over a mean period of 7.3 months. Patients who were in intervention for more than 9 months achieved a weight reduction of 6.3% or 6.8 kg.
Conclusions
This study brings forward evidence of a positive effect of a real-life eHealth lifestyle intervention on diabetes patients’ lifestyle in a municipal setting. Future research is needed to show if the effect is sustainable from a long-term perspective.
Introduction:
The objective of this study was to systematically review the literature and perform a meta-analysis comparing the clinical outcomes of telehealth and usual care in the management of diabetes.
Methods:
Multiple strategies, including database searches (MEDLINE, PsycINFO, PubMed, EMBASE, and CINAHL), searches of related journals and reference tracking, were employed to widely search publications from January 2005 to December 2017. The change in hemoglobin A1c (HbA1c) levels was assessed as the primary outcome, and changes in blood pressure, blood lipids, body mass index (BMI), and quality of life were examined as secondary outcomes.
Results:
Nineteen randomized controlled trials (n = 6294 participants) were selected. Telehealth was more effective than usual care in controlling the glycemic index in diabetes patients (weighted mean difference = -0.22%; 95% confidence intervals, -0.28 to -0.15; P < .001). This intervention showed promise in reducing systolic blood pressure levels (P < .001) and diastolic blood pressure levels (P < .001), while no benefits were observed in the control of BMI (P = .79). For total cholesterol and quality of life, telehealth was similar or superior to usual care.
Conclusion:
Telehealth holds promise for improving the clinical effectiveness of diabetes management. Targeting patients with higher HbA1c (≥9%) levels and delivering more frequent intervention (at least 6 times 1 year) may achieve greater improvement.
Background
Obesity is a highly prevalent condition with important health implications. Face-to-face interventions to treat obesity demand a large number of human resources and time, generating a great burden to individuals and health system. In this context, the internet is an attractive tool for delivering weight loss programs due to anonymity, 24-hour-accessibility, scalability, and reachability associated with Web-based programs.
Objective
We aimed to investigate the effectiveness of Web-based digital health interventions, excluding hybrid interventions and non-Web-based technologies such as text messaging, short message service, in comparison to nontechnology active or inactive (wait list) interventions on weight loss and lifestyle habit changes in individuals with overweight and obesity.
Methods
We searched PubMed or Medline, SciELO, Lilacs, PsychNet, and Web of Science up to July 2018, as well as references of previous reviews for randomized trials that compared Web-based digital health interventions to offline interventions. Anthropometric changes such as weight, body mass index (BMI), waist, and body fat and lifestyle habit changes in adults with overweight and obesity were the outcomes of interest. Random effects meta-analysis and meta-regression were performed for mean differences (MDs) in weight. We rated the risk of bias for each study and the quality of evidence across studies using the Grades of Recommendation, Assessment, Development, and Evaluation approach.
Results
Among the 4071 articles retrieved, 11 were included. Weight (MD −0.77 kg, 95% CI −2.16 to 0.62; 1497 participants; moderate certainty evidence) and BMI (MD −0.12 kg/m2; 95% CI −0.64 to 0.41; 1244 participants; moderate certainty evidence) changes were not different between Web-based and offline interventions. Compared to offline interventions, digital interventions led to a greater short-term (<6 months follow-up) weight loss (MD −2.13 kg, 95% CI −2.71 to −1.55; 393 participants; high certainty evidence), but not in the long-term (MD −0.17 kg, 95% CI −2.10 to 1.76; 1104 participants; moderate certainty evidence). Meta-analysis was not possible for lifestyle habit changes. High risk of attrition bias was identified in 5 studies. For weight and BMI outcomes, the certainty of evidence was moderate mainly due to high heterogeneity, which was mainly attributable to control group differences across studies (R²=79%).
Conclusions
Web-based digital interventions led to greater short-term but not long-term weight loss than offline interventions in overweight and obese adults. Heterogeneity was high across studies, and high attrition rates suggested that engagement is a major issue in Web-based interventions.
Introduction:
In the US, obesity rates are higher in rural areas than in urban areas. Rural access to treatment of obesity is limited by a lack of qualified clinicians and by transportation and financial barriers. We describe a telemedicine weight management programme, Wellness Connect, developed through a partnership of academic clinicians and rural primary care providers in South Carolina, and present utilisation and weight outcomes from seven patient cohorts.
Methods:
Eight bi-weekly sessions were provided via telemedicine videoconferencing for groups of patients at these rural primary care clinics. Protocol-based sessions were led by registered dietitians, exercise physiologists and clinical psychologists at a central urban location.
Results:
Of 138 patients who started the programme, 62% (N = 86) of patients met the criteria for completion. Completers lost an average of 3.5% (standard deviation (SD) = 3.9%) body weight, which was statistically significant (p < .001) and corresponded with an average loss of 3.8 kg (SD = 4.5 kg). There were no differences in weight change among clinics (p = .972). Overall, patients and providers reported satisfaction with the programme and identified several challenges to sustainability.
Discussion:
The use of innovative telemedicine interventions continues to be necessary to alleviate barriers to accessing evidence-based services to reduce chronic diseases and decrease obesity rates among rural populations.
Background
Connected health devices with lifestyle coaching can provide real-time support for people with type 2 diabetes (T2D). However, the intensity of lifestyle coaching needed to achieve outcomes is unknown.
Methods
Livongo provides connected, two-way messaging glucose meters, unlimited blood glucose (BG) test strips, and access to certified diabetes educators. We evaluated the incremental effects of adding lifestyle coaching on BG, estimated HbA1c, and weight. We randomized 330 eligible adults (T2D, HbA1c > 7.5%, BMI ≥ 25) to receive no further intervention (n = 75), a connected scale (n = 115), scale plus lightweight coaching (n = 73), or scale plus intense coaching (n = 67) for 12 weeks. We evaluated the change in outcomes using ANOVA.
Results
Livongo participation alone resulted in improved BG control (mean HbA1c declined: 8.5% to 7.5%, p = 0.01). Mean weight loss and additional BG decreases were higher in the intensive compared with the lightweight coaching and scale-only groups (weight change (lb): −6.4, −4.1, and −1.1, resp., p = 0.01; BG change (mg/dL): −19.4, −11.3, and −2.9, resp., p = 0.02). The estimated 12-week program costs were 5.5 times more for intensive than lightweight coaching.
Conclusion
Livongo participation significantly improves BG control in people with T2D. Additional lifestyle coaching may be a cost-effective intervention to achieve further glucose control and weight loss.
Background:
Wearables, fitness apps, and patient home monitoring devices are used increasingly by patients and other individuals with lifestyle challenges. All Danish general practitioners (GPs) use digital health records and electronic health (eHealth) consultations on a daily basis, but how they perceive the increasing demand for lifestyle advice and whether they see eHealth as part of their lifestyle support should be explored further.
Objective:
This study aimed to explore GPs' perspectives on eHealth devices and apps and the use of eHealth in supporting healthy lifestyle behavior for their patients and themselves.
Methods:
A total of 10 (5 female and 5 male) GPs were recruited by purposive sampling, aged 38 to 69 years (mean 51 years), of which 4 had an urban uptake of patients and 6 a rural uptake. All of them worked in the region of Southern Denmark where GPs typically work alone or in partnership with 1 to 4 colleagues and all use electronic patient health records for prescription, referral, and asynchronous electronic consultations. We performed qualitative, semistructured, individual in-depth interviews with the GPs in their own office about how they used eHealth and mHealth devices to help patients challenged with lifestyle issues and themselves. We also interviewed how they treated lifestyle-challenged patients in general and how they imagined eHealth could be used in the future.
Results:
All GPs had smartphones or tablets, and everyone communicated on a daily basis with patients about disease and medicine via their electronic health record and the internet. We identified 3 themes concerning the use of eHealth: (1) how eHealth is used for patients; (2) general practitioners' own experience with improving lifestyle and eHealth support; and (3) relevant coaching techniques for transformation into eHealth.
Conclusions:
GPs used eHealth frequently for themselves but only infrequently for their patients. GPs are familiar with behavioral change techniques and are ready to use them in eHealth if they are used to optimize processes and not hinder other treatments. Looking ahead, education of GPs and recognizing patients' ability and preference to use eHealth with regard to a healthy living are needed.
Objectives
Assisting patients in lifestyle change using collaborative e-health tools can be an efficient treatment for non-communicable diseases like diabetes, cardiovascular disease and chronic obstructive lung disease that are caused or aggravated by unhealthy living in the form of unhealthy diet, physical inactivity or tobacco smoking. In a prospective pilot study, we tested an online collaborative e-health tool in general practice. The aim of this study was to identify drivers of importance for long-term personal lifestyle changes from a patient perspective when using a collaborative e-health tool, including the support of peers and healthcare professionals.
Setting
General practice clinics in the Region of Southern Denmark.
Participants
10 overweight patients who had previously successfully used a hybrid online collaborative e-health tool with both face-to-face and online consultations to lose weight.
Results
The main themes identified were facilitators, barriers and support from family and peers. Establishment of a trustworthy relationship with the healthcare professionals was of paramount importance. It was important for the patients to monitor the measurable outcomes with realistic goals and feedback from a trusted person. Often, significant life events were identified as catalysts for successful long-term lifestyle changes. Dominant barriers to change were perception of insurmountable obstacles, experience of lack of self-efficacy and excess eating of high-calorie food. Finally, experiencing of trustworthy person-to-person forums, need for acknowledgement from referent others and support from family and peers were important drivers for long-term lifestyle change.
Conclusion
The most important driver in long-term weight loss was a strong relationship with a healthcare professional. Collaborative e-health tools can support the relationship and behavioural changes through monitoring and providing relevant feedback. The support from family and peers also matters, and long-term success depends on the ability to establish strong, positive support on a day-to-day basis.
Background:
Type 2 diabetes is a chronic disorder that requires lifelong treatment. We aimed to assess whether intensive weight management within routine primary care would achieve remission of type 2 diabetes.
Methods:
We did this open-label, cluster-randomised trial (DiRECT) at 49 primary care practices in Scotland and the Tyneside region of England. Practices were randomly assigned (1:1), via a computer-generated list, to provide either a weight management programme (intervention) or best-practice care by guidelines (control), with stratification for study site (Tyneside or Scotland) and practice list size (>5700 or ≤5700). Participants, carers, and research assistants who collected outcome data were aware of group allocation; however, allocation was concealed from the study statistician. We recruited individuals aged 20-65 years who had been diagnosed with type 2 diabetes within the past 6 years, had a body-mass index of 27-45 kg/m2, and were not receiving insulin. The intervention comprised withdrawal of antidiabetic and antihypertensive drugs, total diet replacement (825-853 kcal/day formula diet for 3-5 months), stepped food reintroduction (2-8 weeks), and structured support for long-term weight loss maintenance. Co-primary outcomes were weight loss of 15 kg or more, and remission of diabetes, defined as glycated haemoglobin (HbA1c) of less than 6·5% (<48 mmol/mol) after at least 2 months off all antidiabetic medications, from baseline to 12 months. These outcomes were analysed hierarchically. This trial is registered with the ISRCTN registry, number 03267836.
Findings:
Between July 25, 2014, and Aug 5, 2017, we recruited 306 individuals from 49 intervention (n=23) and control (n=26) general practices; 149 participants per group comprised the intention-to-treat population. At 12 months, we recorded weight loss of 15 kg or more in 36 (24%) participants in the intervention group and no participants in the control group (p<0·0001). Diabetes remission was achieved in 68 (46%) participants in the intervention group and six (4%) participants in the control group (odds ratio 19·7, 95% CI 7·8-49·8; p<0·0001). Remission varied with weight loss in the whole study population, with achievement in none of 76 participants who gained weight, six (7%) of 89 participants who maintained 0-5 kg weight loss, 19 (34%) of 56 participants with 5-10 kg loss, 16 (57%) of 28 participants with 10-15 kg loss, and 31 (86%) of 36 participants who lost 15 kg or more. Mean bodyweight fell by 10·0 kg (SD 8·0) in the intervention group and 1·0 kg (3·7) in the control group (adjusted difference -8·8 kg, 95% CI -10·3 to -7·3; p<0·0001). Quality of life, as measured by the EuroQol 5 Dimensions visual analogue scale, improved by 7·2 points (SD 21·3) in the intervention group, and decreased by 2·9 points (15·5) in the control group (adjusted difference 6·4 points, 95% CI 2·5-10·3; p=0·0012). Nine serious adverse events were reported by seven (4%) of 157 participants in the intervention group and two were reported by two (1%) participants in the control group. Two serious adverse events (biliary colic and abdominal pain), occurring in the same participant, were deemed potentially related to the intervention. No serious adverse events led to withdrawal from the study.
Interpretation:
Our findings show that, at 12 months, almost half of participants achieved remission to a non-diabetic state and off antidiabetic drugs. Remission of type 2 diabetes is a practical target for primary care.
Funding:
Diabetes UK.
Background:
Despite rapid growth in eHealth research, there remains a lack of consistency in defining and using terms related to eHealth. More widely cited definitions provide broad understanding of eHealth but lack sufficient conceptual clarity to operationalize eHealth and enable its implementation in health care practice, research, education, and policy. Definitions that are more detailed are often context or discipline specific, limiting ease of translation of these definitions across the breadth of eHealth perspectives and situations. A conceptual model of eHealth that adequately captures its complexity and potential overlaps is required. This model must also be sufficiently detailed to enable eHealth operationalization and hypothesis testing.
Objective:
This study aimed to develop a conceptual practice-based model of eHealth to support health professionals in applying eHealth to their particular professional or discipline contexts.
Methods:
We conducted semistructured interviews with key informants (N=25) from organizations involved in health care delivery, research, education, practice, governance, and policy to explore their perspectives on and experiences with eHealth. We used purposeful sampling for maximum diversity. Interviews were coded and thematically analyzed for emergent domains.
Results:
Thematic analyses revealed 3 prominent but overlapping domains of eHealth: (1) health in our hands (using eHealth technologies to monitor, track, and inform health), (2) interacting for health (using digital technologies to enable health communication among practitioners and between health professionals and clients or patients), and (3) data enabling health (collecting, managing, and using health data). These domains formed a model of eHealth that addresses the need for clear definitions and a taxonomy of eHealth while acknowledging the fluidity of this area and the strengths of initiatives that span multiple eHealth domains.
Conclusions:
This model extends current understanding of eHealth by providing clearly defined domains of eHealth while highlighting the benefits of using digital technologies in ways that cross several domains. It provides the depth of perspectives and examples of eHealth use that are lacking in previous research. On the basis of this model, we suggest that eHealth initiatives that are most impactful would include elements from all 3 domains.
Importance
It is unclear whether a lifestyle intervention can maintain glycemic control in patients with type 2 diabetes.
Objective
To test whether an intensive lifestyle intervention results in equivalent glycemic control compared with standard care and, secondarily, leads to a reduction in glucose-lowering medication in participants with type 2 diabetes.
Design, Setting, and Participants
Randomized, assessor-blinded, single-center study within Region Zealand and the Capital Region of Denmark (April 2015-August 2016). Ninety-eight adult participants with non–insulin-dependent type 2 diabetes who were diagnosed for less than 10 years were included. Participants were randomly assigned (2:1; stratified by sex) to the lifestyle group (n = 64) or the standard care group (n = 34).
Interventions
All participants received standard care with individual counseling and standardized, blinded, target-driven medical therapy. Additionally, the lifestyle intervention included 5 to 6 weekly aerobic training sessions (duration 30-60 minutes), of which 2 to 3 sessions were combined with resistance training. The lifestyle participants received dietary plans aiming for a body mass index of 25 or less. Participants were followed up for 12 months.
Main Outcomes and Measures
Primary outcome was change in hemoglobin A1c (HbA1c) from baseline to 12-month follow-up, and equivalence was prespecified by a CI margin of ±0.4% based on the intention-to-treat population. Superiority analysis was performed on the secondary outcome reductions in glucose-lowering medication.
Results
Among 98 randomized participants (mean age, 54.6 years [SD, 8.9]; women, 47 [48%]; mean baseline HbA1c, 6.7%), 93 participants completed the trial. From baseline to 12-month follow-up, the mean HbA1c level changed from 6.65% to 6.34% in the lifestyle group and from 6.74% to 6.66% in the standard care group (mean between-group difference in change of −0.26% [95% CI, −0.52% to −0.01%]), not meeting the criteria for equivalence (P = .15). Reduction in glucose-lowering medications occurred in 47 participants (73.5%) in the lifestyle group and 9 participants (26.4%) in the standard care group (difference, 47.1 percentage points [95% CI, 28.6-65.3]). There were 32 adverse events (most commonly musculoskeletal pain or discomfort and mild hypoglycemia) in the lifestyle group and 5 in the standard care group.
Conclusions and Relevance
Among adults with type 2 diabetes diagnosed for less than 10 years, a lifestyle intervention compared with standard care resulted in a change in glycemic control that did not reach the criterion for equivalence, but was in a direction consistent with benefit. Further research is needed to assess superiority, as well as generalizability and durability of findings.
Trial Registration
clinicaltrials.gov Identifier: NCT02417012
Background:
Rising obesity levels remain a major public health concern due to the clear link with several comorbidities such as diabetes. Diabetes now affects 6% of the UK population. Modest weight loss of 5% to 10% has been shown to be associated with significant reductions in blood sugar, lipid, and blood pressure levels. Men have been shown to be attracted to programs that do not require extensive face-to-face time commitments, illustrating the potential audience available for health behavior change via the Web.
Objective:
The objective of our study was to evaluate the feasibility and acceptability of a Web-based weight loss intervention in men with type 2 diabetes.
Methods:
We conducted a pilot, parallel 2-arm, individually randomized controlled trial with embedded process evaluation. Participants were randomly assigned in a one-to-one ratio to the usual care group or the 12-month Web-based weight loss intervention, including dietitian and exercise expert feedback. Face-to-face recruitment and assessment were performed by the researcher unblinded. Data collected included weight, height, body mass index (BMI), and waist circumference, together with an audit trail of eligibility, recruitment, retention, and adherence rates. A process evaluation (website use data and qualitative interviews) monitored adherence, acceptability, and feasibility of the intervention.
Results:
General practice database searches achieved the recruitment target (n=61) for the population of men with type 2 diabetes, of whom 66% (40/61) completed 3-month follow-up measurements. By 12 months, the retention rate was 52% (32/61), with 12 of the 33 men allocated to the intervention group still active on the website. The intervention was seen as acceptable by the majority of participants. We gained insights about acceptability and use of the website from the parallel process evaluation.
Conclusions:
Recruitment to the Web-based weight loss intervention was successful. Results are descriptive, but there were positive indications of increased weight loss (in kilograms and as a percentage), and reduced waist circumference and BMI for the intervention group from 3 to 12 months, in comparison with control. This research adds to the evidence base in relation to incorporating a Web-based weight loss intervention within the UK National Health Service (NHS). NHS weight loss services are struggling to provide sufficient referrals. Therefore, alternative modes of delivery, with the potential to reduce health professional input and time per patient while still enabling individual and tailored care, need to be investigated to identify whether they can be effective and thus benefit the NHS.
Trial registration:
International Standard Randomized Controlled Trial Number (ISRCTN): 48086713; http://www.isrctn.com/ISRCTN48086713 (Archived by WebCite at http://www.webcitation.org/6rO4xSlhI).
Purpose of review:
One begins to see improvement in glycemic measures and triglycerides with small amounts of weight loss, but with greater levels of weight loss there is even greater improvement. In fact, the relationship between weight loss and glycemia is one that is very close.
Recent findings:
This is fortunate for diabetes prevention; it takes only small amounts of weight loss to prevent progression to type 2 diabetes from impaired glucose tolerance, and after the 10 kg of weight loss, one cannot demonstrate much additional improvement in risk reduction. Modest weight loss (5 to 10%) is also associated with improvement in systolic and diastolic blood pressure and HDL cholesterol. With all these risk factors, more weight loss produces more improvement. Further, for patients with higher BMI levels (>40 kg/m(2)), the ability to lose the same proportion of weight with lifestyle intervention is equal to that of those with lower BMI levels, and there is equal benefit in terms of risk factor improvement with modest weight loss. For some comorbid conditions, more weight loss is needed-10 to 15%-to translate into clinical improvement. This is true with obstructive sleep apnea and non-alcoholic steatotic hepatitis. There is a graded improvement in improvements in measures of quality of life, depression, mobility, sexual dysfunction, and urinary stress incontinence, whereby improvements are demonstrable with modest weight loss (5-10%) and with further weight loss there are further improvements. For polycystic ovarian syndrome and infertility, modest weight loss (beginning at 2-5%) can bring improvements in menstrual irregularities and fertility. Moderate weight loss (5-10%) has been shown to be associated with reduced health care costs. Reduction in mortality may take more than 10% weight loss, although definitive studies have not been done to demonstrate that weight loss per se is associated with mortality reduction. Clinicians in medical weight management should bear in mind that the target should be health improvement rather than a number on the scale. The individual patient's targeted health goal should be assessed for response rather than a prescribed percentage weight loss.
Introduction
With increasing development and use of mobile health (mHealth) interventions for weight loss in overweight and obese populations, it is timely to gain greater insight into consumer experience with these technologies. The aims of this review were to identify common themes across studies that included user preferences for mHealth intervention for weight loss.
Methods
The databases PubMed (Medline), CINAHL, Web of Science, and Embase were searched for relevant qualitative studies on mHealth for weight loss. Searches were conducted in May 2016.
Results
Several common high preference themes were identified relating to simple and attractive apps that allowed for self-monitoring with feedback. The five key themes concerning text messages for weight loss involved a careful consideration of personalization, message tone, structure, frequency and content. Key optimization themes for weight loss apps were personalization, simplicity with appeal and engagement/entertainment. Common identified benefits of mHealth for weight loss included self-monitoring, goal setting, feedback, ability to motivate, educate, and remind. Common barriers users identified were related to technological and psychological issues as well as message overload/inappropriate timing of messages.
Conclusion
When planning an mHealth weight loss intervention, critical factors are the message tone, structure and the frequency of message delivery. Personalization also seems to be important. Designing simple apps while still ensuring that they engage the user is also essential. Additionally, it seems important to tailor the content in accordance with different target group demographic preferences. The successful reach and adoption of mHealth interventions requires minimizing perceived barriers and maximizing perceived benefits.
Background
Novel interventions are needed to improve lifestyle and prevent noncommunicable diseases, the leading cause of death and disability globally. This study aimed to systematically review, synthesize, and grade scientific evidence on effectiveness of novel information and communication technology to reduce noncommunicable disease risk.
Methods and Results
We systematically searched PubMed for studies evaluating the effect of Internet, mobile phone, personal sensors, or stand‐alone computer software on diet, physical activity, adiposity, tobacco, or alcohol use. We included all interventional and prospective observational studies conducted among generally healthy adults published between January 1990 and November 2013. American Heart Association criteria were used to evaluate and grade the strength of evidence. From 8654 abstracts, 224 relevant reports were identified. Internet and mobile interventions were most common. Internet interventions improved diet (N=20 studies) (Class IIa A), physical activity (N=33), adiposity (N=35), tobacco (N=22), and excess alcohol (N=47) (Class I A each). Mobile interventions improved physical activity (N=6) and adiposity (N=3) (Class I A each). Evidence limitations included relatively brief durations (generally <6 months, nearly always <1 year), heterogeneity in intervention content and intensity, and limited representation from middle/low‐income countries.
Conclusions
Internet and mobile interventions improve important lifestyle behaviors up to 1 year. This systematic review supports the need for long‐term interventions to evaluate sustainability.
Background:
Obesity levels continue to rise annually. Face-to-face weight loss consultations have previously identified mixed effectiveness and face high demand with limited resources. Therefore, alternative interventions, such as internet-delivered interventions, warrant further investigation. The aim was to assess whether internet-delivered weight loss interventions providing personalized feedback were more effective for weight loss in overweight and obese adults in comparison with control groups receiving no personalized feedback.
Method:
Nine databases were searched, and 12 studies were identified that met all inclusion criteria.
Results:
Meta-analysis, identified participants receiving personalized feedback via internet-delivered interventions, had 2.13 kg mean difference (SMD) greater weight loss (and BMI change, waist circumference change and 5% weight loss) in comparison with control groups providing no personalized feedback. This was also true for results at 3 and 6-month time points but not for studies where interventions lasted ≥12 months.
Conclusion:
This suggests that personalized feedback may be an important behaviour change technique (BCT) to incorporate within internet-delivered weight loss interventions. However, meta-analysis results revealed no differences between internet-delivered weight loss interventions with personalized feedback and control interventions ≥12 months. Further investigation into longer term internet-delivered interventions is required to examine how weight loss could be maintained. Future research examining which BCTs are most effective for internet-delivered weight loss interventions is suggested.
Objective:
Common factors such as therapist empathy play an important role in treatment for addictive behaviors. The present study was a secondary analysis designed to evaluate the relation between therapist empathy and alcohol treatment outcomes in data from a large, multisite, randomized controlled trial.
Method:
Audio-recorded psychotherapy sessions for 38 therapists and 700 clients had been randomly selected for fidelity coding from the combined behavioral intervention condition of Project COMBINE. Sessions were evaluated by objective raters for both specific content (coping with craving, building social skills, and managing negative mood) and relational components (empathy level of the therapist). Multilevel modeling with clients nested within therapists evaluated drinks per week at the end of treatment.
Results:
Approximately 11% of the variance in drinking was accounted for by therapists. A within-therapist effect of empathy was detected (B = -0.381, SE = 0.103, p < .001); more empathy than usual was associated with subsequent decreased drinking. The Social and Recreational Counseling module (B = -0.412, SE = 0.124, p < .001), Coping with Cravings and Urges module (B = -0.362, SE = 0.134, p < .01), and the Mood Management module (B = -0.403, SE = 0.138, p < .01) were also associated with decreased drinking. No between-therapist effect was detected, and the Empathy × Module Content interactions were not significant.
Conclusions:
The results of the study appear consistent with the hypothesis that skills building and therapist empathy are independent contributions to the overall benefit derived from the combined behavioral intervention. (PsycINFO Database Record
Background:
One-third of US adults, 86 million people, have prediabetes. Two-thirds of adults are overweight or obese and at risk for diabetes. Effective and affordable interventions are needed that can reach these 86 million, and others at high risk, to reduce their progression to diagnosed diabetes.
Objective:
The aim was to evaluate the effectiveness of a fully automated algorithm-driven behavioral intervention for diabetes prevention, Alive-PD, delivered via the Web, Internet, mobile phone, and automated phone calls.
Methods:
Alive-PD provided tailored behavioral support for improvements in physical activity, eating habits, and factors such as weight loss, stress, and sleep. Weekly emails suggested small-step goals and linked to an individual Web page with tools for tracking, coaching, social support through virtual teams, competition, and health information. A mobile phone app and automated phone calls provided further support. The trial randomly assigned 339 persons to the Alive-PD intervention (n=163) or a 6-month wait-list usual-care control group (n=176). Participants were eligible if either fasting glucose or glycated hemoglobin A1c (HbA1c) was in the prediabetic range. Primary outcome measures were changes in fasting glucose and HbA1c at 6 months. Secondary outcome measures included clinic-measured changes in body weight, body mass index (BMI), waist circumference, triglyceride/high-density lipoprotein cholesterol (TG/HDL) ratio, and Framingham diabetes risk score. Analysis was by intention-to-treat.
Results:
Participants' mean age was 55 (SD 8.9) years, mean BMI was 31.2 (SD 4.4) kg/m(2), and 68.7% (233/339) were male. Mean fasting glucose was in the prediabetic range (mean 109.9, SD 8.4 mg/dL), whereas the mean HbA1c was 5.6% (SD 0.3), in the normal range. In intention-to-treat analyses, Alive-PD participants achieved significantly greater reductions than controls in fasting glucose (mean -7.36 mg/dL, 95% CI -7.85 to -6.87 vs mean -2.19, 95% CI -2.64 to -1.73, P<.001), HbA1c (mean -0.26%, 95% CI -0.27 to -0.24 vs mean -0.18%, 95% CI -0.19 to -0.16, P<.001), and body weight (mean -3.26 kg, 95% CI -3.26 to -3.25 vs mean -1.26 kg, 95% CI -1.27 to -1.26, P<.001). Reductions in BMI, waist circumference, and TG/HDL were also significantly greater in Alive-PD participants than in the control group. At 6 months, the Alive-PD group reduced their Framingham 8-year diabetes risk from 16% to 11%, significantly more than the control group (P<.001). Participation and retention was good; intervention participants interacted with the program a median of 17 (IQR 14) of 24 weeks and 71.1% (116/163) were still interacting with the program in month 6.
Conclusions:
Alive-PD improved glycemic control, body weight, BMI, waist circumference, TG/HDL ratio, and diabetes risk. As a fully automated system, the program has high potential for scalability and could potentially reach many of the 86 million US adults who have prediabetes as well as other at-risk groups.
Trial registration:
Clinicaltrials.gov NCT01479062; https://clinicaltrials.gov/ct2/show/NCT01479062 (Archived by WebCite at http://www.webcitation.org/6bt4V20NR).
Background:
Adoptions of health behaviors are crucial for maintaining good health after type 2 diabetes mellitus (T2DM) diagnoses. However, adherence to glucoregulating behaviors like regular exercise and balanced diet can be challenging, especially for people living in lower-socioeconomic status (SES) communities. Providing cost-effective interventions that improve self-management is important for improving quality of life and the sustainability of health care systems.
Objective:
To evaluate a health coach intervention with and without the use of mobile phones to support health behavior change in patients with type 2 diabetes.
Methods:
In this noninferiority, pragmatic randomized controlled trial (RCT), patients from two primary care health centers in Toronto, Canada, with type 2 diabetes and a glycated hemoglobin/hemoglobin A1c (HbA1c) level of ≥7.3% (56.3 mmol/mol) were randomized to receive 6 months of health coaching with or without mobile phone monitoring support. We hypothesized that both approaches would result in significant HbA1c reductions, although health coaching with mobile phone monitoring would result in significantly larger effects. Participants were evaluated at baseline, 3 months, and 6 months. The primary outcome was the change in HbA1c from baseline to 6 months (difference between and within groups). Other outcomes included weight, waist circumference, body mass index (BMI), satisfaction with life, depression and anxiety (Hospital Anxiety and Depression Scale [HADS]), positive and negative affect (Positive and Negative Affect Schedule [PANAS]), and quality of life (Short Form Health Survey-12 [SF-12]).
Results:
A total of 138 patients were randomized and 7 were excluded for a substudy; of the remaining 131, 67 were allocated to the intervention group and 64 to the control group. Primary outcome data were available for 97 participants (74.0%). While both groups reduced their HbA1c levels, there were no significant between-group differences in change of HbA1c at 6 months using intention-to-treat (last observation carried forward [LOCF]) (P=.48) or per-protocol (P=.83) principles. However, the intervention group did achieve an accelerated HbA1c reduction, leading to a significant between-group difference at 3 months (P=.03). This difference was reduced at the 6-month follow-up as the control group continued to improve, achieving a reduction of 0.81% (8.9 mmol/mol) (P=.001) compared with a reduction of 0.84% (9.2 mmol/mol)(P=.001) in the intervention group. Intervention group participants also had significant decreases in weight (P=.006) and waist circumference (P=.01) while controls did not. Both groups reported improvements in mood, satisfaction with life, and quality of life.
Conclusions:
Health coaching with and without access to mobile technology appeared to improve glucoregulation and mental health in a lower-SES, T2DM population. The accelerated improvement in the mobile phone group suggests the connectivity provided may more quickly improve adoption and adherence to health behaviors within a clinical diabetes management program. Overall, health coaching in primary care appears to lead to significant benefits for patients from lower-SES communities with poorly controlled type 2 diabetes.
Trial registration:
ClinicalTrials.gov NCT02036892; http://clinicaltrials.gov/ct2/show/NCT02036892 (Archived by WebCite at http://www.webcitation.org/6b3cJYJOD).
Background:
The US Preventive Services Task Force recommends screening for and treating obesity. However, there are many barriers to successfully treating obesity in primary care (PC). Technology-assisted weight loss interventions offer novel ways of improving treatment, but trials are overwhelmingly conducted outside of PC and may not translate well into this setting. We conducted a systematic review of technology-assisted weight loss interventions specifically tested in PC settings.
Methods:
We searched the literature from January 2000 to March 2014.
Inclusion criteria:
(1) Randomized controlled trial; (2) trials that utilized the Internet, personal computer, and/or mobile device; and (3) occurred in an ambulatory PC setting. We applied the Cochrane Effective Practice and Organization of Care (EPOC) and Delphi criteria to assess bias and the Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) criteria to assess pragmatism (whether trials occurred in the real world versus under ideal circumstances). Given heterogeneity, results were not pooled quantitatively.
Results:
Sixteen trials met inclusion criteria. Twelve (75 %) interventions achieved weight loss (range: 0.08 kg - 5.4 kg) compared to controls, while 5-45 % of patients lost at least 5 % of baseline weight. Trial duration and attrition ranged from 3-36 months and 6-80 %, respectively. Ten (63 %) studies reported results after at least 1 year of follow-up. Interventions used various forms of personnel, technology modalities, and behavior change elements; trials most frequently utilized medical doctors (MDs) (44 %), web-based applications (63 %), and self-monitoring (81 %), respectively. Interventions that included clinician-guiding software or feedback from personnel appeared to promote more weight loss than fully automated interventions. Only two (13 %) studies used publically available technologies. Many studies had fair pragmatism scores (mean: 2.8/4), despite occurring in primary care.
Discussion:
Compared to usual care, technology-assisted interventions in the PC setting help patients achieve weight loss, offering evidence-based options to PC providers. However, best practices remain undetermined. Despite occurring in PC, studies often fall short in utilizing pragmatic methodology and rarely provide publically available technology. Longitudinal, pragmatic, interdisciplinary, and open-source interventions are needed.
Without a complete published description of interventions, clinicians and patients cannot reliably implement interventions that are shown to be useful, and other researchers cannot replicate or build on research findings. The quality of description of interventions in publications, however, is remarkably poor. To improve the completeness of reporting, and ultimately the replicability, of interventions, an international group of experts and stakeholders developed the Template for Intervention Description and Replication (TIDieR) checklist and guide. The process involved a literature review for relevant checklists and research, a Delphi survey of an international panel of experts to guide item selection, and a face-to-face panel meeting. The resultant 12-item TIDieR checklist (brief name, why, what (materials), what (procedure), who intervened, how, where, when and how much, tailoring, modifications, how well (planned), how well (actually carried out)) is an extension of the CONSORT 2010 statement (item 5) and the SPIRIT 2013 statement (item 11). While the emphasis of the checklist is on trials, the guidance is intended to apply across all evaluative study designs. This paper presents the TIDieR checklist and guide, with a detailed explanation of each item, and examples of good reporting. The TIDieR checklist and guide should improve the reporting of interventions and make it easier for authors to structure the accounts of their interventions, reviewers and editors to assess the descriptions, and readers to use the information.
© Georg Thieme Verlag KG Stuttgart · New York.
One of the largest determinants of client outcomes is the counselor who provides treatment. Therapists often vary widely in effectiveness, even when delivering standardized manual-guided treatment. In particular, the therapeutic skill of accurate empathy originally described by Carl Rogers has been found to account for a meaningful proportion of variance in therapeutic alliance and in addiction treatment outcomes. High-empathy counselors appear to have higher success rates regardless of theoretical orientation. Low-empathy and confrontational counseling, in contrast, has been associated with higher drop-out and relapse rates, weaker therapeutic alliance, and less client change. The authors propose emphasis on empathic listening skills as an evidence-based practice in the hiring and training of counselors to improve outcomes and prevent harm in addiction treatment.
This article introduces the new 5-level EQ-5D (EQ-5D-5L) health status measure.
EQ-5D currently measures health using three levels of severity in five dimensions. A EuroQol Group task force was established to find ways of improving the instrument's sensitivity and reducing ceiling effects by increasing the number of severity levels. The study was performed in the United Kingdom and Spain. Severity labels for 5 levels in each dimension were identified using response scaling. Focus groups were used to investigate the face and content validity of the new versions, including hypothetical health states generated from those versions.
Selecting labels at approximately the 25th, 50th, and 75th centiles produced two alternative 5-level versions. Focus group work showed a slight preference for the wording 'slight-moderate-severe' problems, with anchors of 'no problems' and 'unable to do' in the EQ-5D functional dimensions. Similar wording was used in the Pain/Discomfort and Anxiety/Depression dimensions. Hypothetical health states were well understood though participants stressed the need for the internal coherence of health states.
A 5-level version of the EQ-5D has been developed by the EuroQol Group. Further testing is required to determine whether the new version improves sensitivity and reduces ceiling effects.
BACKGROUND
Long-term weight loss among subjects with obesity can reduce the risk and progression of noncommunicable diseases (NCDs). Observational studies suggest that digital coaching can lead to long-term weight loss.
OBJECTIVE
We investigated whether an eHealth lifestyle coaching program (LIVA) for subjects with obesity with or without type 2 diabetes (T2D) leads to significant long-term (more than 6 months) weight loss compared to usual care.
METHODS
In a randomized controlled trial that took place in 50 municipalities in Denmark, 340 subjects with obesity with or without T2D were enrolled from April 16, 2018, to April 1, 2019, and randomized via an automated computer algorithm to an intervention (200) or a control (140) group. Patients were recruited via their general practitioners, the Danish diabetes organization, and social media. The digital coaching intervention comprised of an initial one-hour face-to-face motivational interview followed by digital coaching using behavioral change techniques enabled by individual live monitoring. Primary outcome was change in body weight from baseline to 6 and 12 months.
RESULTS
Data were assessed for 235 participants, 149 from the intervention group and 86 from the control group who completed 6 and/or 12 months follow-up. After 12 months mean body weight and body mass index were reduced significantly in both groups but significantly more in the intervention group (–4.6 (-5.7; -3.4) kg vs. -1.4 (-2.6; -0.1) kg, P<.001 and -1.5 (-1.9; -1.2) kg/m2 vs. -0.5 (-0.9; -0.1) kg/m2, P<.001). Hemoglobin A1c (HbA1c) was significantly reduced in both the intervention (-6.0 (-7.7; -4.3) mmol/mol) and the control group (-4.9 (-7.4; -2.4) mmol/mol) without significant group difference.
CONCLUSIONS
Compared to usual care, digital lifestyle coaching can induce significant weight loss in obese subjects both with and without T2D after 12 months.
CLINICALTRIAL
ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915
Aims
To compare the associations between four anthropometric indices including waist-to-height ratio (WHtR), waist circumference (WC), waist-hip-ratio (WHR) and body mass index (BMI) and cardio-cerebrovascular events (CCBVEs) in Chinese T2DM patients.
Methods
The associations of four anthropometric measures with CCBVEs and metabolic syndrome (MetS) were compared by multiple regression model in 3108 T2DM patients. CCBVEs was defined as a history of myocardial infarction, angina, angioplasty, coronary artery bypass surgery, transient ischemic attack, ischemic or hemorrhagic stroke.
Results
After controlling for age, sex and diabetes duration, the prevalence of CCBVEs and MetS significantly increased across the WHtR, WC, WHR and BMI quartiles in T2DM patients, respectively. However, when controlling for these four anthropometric measurements together, although four anthropometric measures were closely associated with MetS prevalence, only WHtR quartile was significantly associated with CCBVEs prevalence (6.5%, 13.8%, 16.9% and 21.3%, p<0.001 for trend). After adjusting for multiple confounders including four anthropometric parameters, a regression analysis revealed that only WHtR was independently and positively associated with the presence of CCBVEs (p=0.029).
Conclusions
Compared with WC, WHR and BMI, WHtR have a stronger association with CCBVEs in T2DM subjects. WHtR maybe a better indicator than other anthropometric measurements for evaluating cardiovascular risks in T2DM.
Background
: Evidence-based guidelines for the management of type 2 diabetes (T2D) consist of blood glucose monitoring, medication adherence, and lifestyle modifications that may particularly benefit from reminders, consultation, education, and behavioral reinforcements through remote patient monitoring (RPM).
Objectives
: To identify predictors of weight loss and to examine the association between weight loss and hemoglobin A1C (HbA1C) outcomes for T2D patients who were enrolled in an RPM program for diabetes management.
Materials and Methods
: The study applied logistic and ordinary least-squares regression models to examine the relationship between baseline characteristics and the likelihood of weight loss during the RPM, and how the magnitude of weight loss was related to changes in HbA1C outcomes for 1,103 T2D patients who went through 3 months of RPM from 2014 to 2017.
Results
: Older patients were 3% more likely to have weight loss (odds ratio [OR], 1.03; 95% confidence interval [CI], 1.02-1.05), whereas patients with higher baseline HbA1C had 9% reduced odds (OR, 0.91; 95% CI, 0.85-0.97) of experiencing weight loss. For every pound of weight lost, there was a 0.02-point (95% CI, 0.01-0.03) reduction on the HbA1C measured at the end of the RPM. Moreover, compared with those who had weight loss of ≤3%, participants who had lost 5-7%, or >7% of their baseline weight had a 0.37- and 0.58-point reduction in HbA1C, respectively.
Conclusions
: This study revealed a notable relationship between weight loss and positive HbA1C outcomes for T2D patients in an RPM-facilitated diabetes management program, which pointed to the potential of integrating evidence-based lifestyle modification programs into future telemedicine programs to improve diabetes management outcomes.
Background:
Lifestyle interventions can delay the onset of type 2 diabetes in people with impaired glucose tolerance, but whether this leads subsequently to fewer complications or to increased longevity is uncertain. We aimed to assess the long-term effects of lifestyle interventions in people with impaired glucose tolerance on the incidence of diabetes, its complications, and mortality.
Methods:
The original study was a cluster randomised trial, started in 1986, in which 33 clinics in Da Qing, China, were randomly assigned to either be a control clinic or provide one of three interventions (diet, exercise, or diet plus exercise) for 6 years for 577 adults with impaired glucose tolerance who usually receive their medical care from the clinics. Subsequently, participants were followed for up to 30 years to assess the effects of intervention on the incidence of diabetes, cardiovascular disease events, composite microvascular complications, cardiovascular disease death, all-cause mortality, and life expectancy.
Findings:
Of the 577 participants, 438 were assigned to an intervention group and 138 to the control group (one refused baseline examination). After 30 years of follow-up, 540 (94%) of 576 participants were assessed for outcomes (135 in the control group, 405 in the intervention group). During the 30-year follow-up, compared with control, the combined intervention group had a median delay in diabetes onset of 3·96 years (95% CI 1·25 to 6·67; p=0·0042), fewer cardiovascular disease events (hazard ratio 0·74, 95% CI 0·59-0·92; p=0·0060), a lower incidence of microvascular complications (0·65, 0·45-0·95; p=0·025), fewer cardiovascular disease deaths (0·67, 0·48-0·94; p=0·022), fewer all-cause deaths (0·74, 0·61-0·89; p=0·0015), and an average increase in life expectancy of 1·44 years (95% CI 0·20-2·68; p=0·023).
Interpretation:
Lifestyle intervention in people with impaired glucose tolerance delayed the onset of type 2 diabetes and reduced the incidence of cardiovascular events, microvascular complications, and cardiovascular and all-cause mortality, and increased life expectancy. These findings provide strong justification to continue to implement and expand the use of such interventions to curb the global epidemic of type 2 diabetes and its consequences.
Funding:
US Centers for Disease Control and Prevention, WHO, Chinese Center for Disease Control and Prevention, World Bank, Ministry of Public Health of the People's Republic of China, Da Qing First Hospital, China-Japan Friendship Hospital, and National Center for Cardiovascular Diseases & Fuwai Hospital.
We conducted a systematic review with meta‐analysis of randomized controlled trials that evaluated the effect of diabetes apps. 1550 participants from 21 studies were included. For type 1 diabetes, a significant 0.49% reduction in HbA1c was seen (95%CI 0.04 to 0.94; I²=84%), with unexplained heterogeneity and a low GRADE of evidence. For type 2 diabetes, using diabetes apps was associated with a mean reduction of 0.57% (95%CI 0.32 to 0.82, I²=77%). The results had severe heterogeneity that was explained by the frequency of HCP feedback. In studies with no HCP feedback, low frequency, and high frequency HCP feedback, the mean reduction is 0.24% (95%CI ‐0.02 to 0.49; I²=0%), 0.33% (95%CI 0.07 to 0.59; I²=47%), and 1.12% (95%CI 0.91 to 1.32; I²=0%) respectively, with high GRADE of evidence. There is evidence that diabetes apps improve glycemic control in type 1 diabetes patients. A reduction of 0.57% in HbA1c was found in type 2 diabetes patients. However, HCP functionality is important to achieve clinical effectiveness. Futures studies need to explore the cost‐effectiveness of diabetes apps and optimal intensity of HCP feedback.
Weight loss can be achieved through a variety of modalities, but long-term maintenance of lost weight is much more challenging. Obesity interventions typically result in early weight loss followed by a weight plateau and progressive regain. This review describes current understanding of the biological, behavioral, and environmental factors driving this near-ubiquitous body weight trajectory and the implications for long-term weight management. Treatment of obesity requires ongoing clinical attention and weight maintenance-specific counseling to support sustainable healthful behaviors and positive weight regulation.
The objective was to describe Diabetes Prevention Program (DPP)-based lifestyle interventions delivered via electronic, mobile, and certain types of telehealth (eHealth) and estimate the magnitude of the effect on weight loss. A systematic review was conducted. PubMed and EMBASE were searched for studies published between January 2003 and February 2016 that met inclusion and exclusion criteria. An overall estimate of the effect on mean percentage weight loss across all the interventions was initially conducted. A stratified meta-analysis was also conducted to determine estimates of the effect across the interventions classified according to whether behavioral support by counselors post-baseline was not provided, provided remotely with communication technology, or face-to-face.
Twenty-two studies met the inclusion/exclusion criteria, in which 26 interventions were evaluated. Samples were primarily white and college educated. Interventions included Web-based applications, mobile phone applications, text messages, DVDs, interactive voice response telephone calls, telehealth video conferencing, and video on-demand programing. Nine interventions were stand-alone, delivered post-baseline exclusively via eHealth. Seventeen interventions included additional behavioral support provided by counselors post-baseline remotely with communication technology or face-to-face. The estimated overall effect on mean percentage weight loss from baseline to up to 15 months of follow-up across all the interventions was − 3.98%. The subtotal estimate across the stand-alone eHealth interventions (− 3.34%) was less than the estimate across interventions with behavioral support given by a counselor remotely (− 4.31%), and the estimate across interventions with behavioral support given by a counselor in-person (− 4.65%).
There is promising evidence of the efficacy of DPP-based eHealth interventions on weight loss. Further studies are needed particularly in racially and ethnically diverse populations with limited levels of educational attainment. Future research should also focus on ways to optimize behavioral support.
Aims:
To investigate a virtual assistance-based lifestyle intervention to reduce risk factors for Type 2 diabetes in young employees in the information technology industry in India.
Methods:
LIMIT (Lifestyle Modification in Information Technology) was a parallel-group, partially blinded, randomized controlled trial. Employees in the information technology industry with ≥3 risk factors (family history of cardiometabolic disease, overweight/obesity, high blood pressure, impaired fasting glucose, hypertriglyceridaemia, high LDL cholesterol and low HDL cholesterol) from two industrieswere randomized to a control or an intervention (1:1) group. After initial lifestyle advice, the intervention group additionally received reinforcement through mobile phone messages (three per week) and e-mails (two per week) for 1 year. The primary outcome was change in prevalence of overweight/obesity, analysed by intention to treat.
Results:
Of 437 employees screened (mean age 36.2 ± 9.3 years; 74.8% men), 265 (61.0%) were eligible and randomized into the control (n=132) or intervention (n=133) group. After 1 year, the prevalence of overweight/obesity reduced by 6.0% in the intervention group and increased by 6.8% in the control group (risk difference 11.2%; 95% CI 1.2-21.1; P=0.042). There were also significant improvements in lifestyle measurements, waist circumference, and total and LDL cholesterol in the intervention group. The number-needed-to-treat to prevent one case of overweight/obesity in 1 year was 9 (95% CI 5-82), with an incremental cost of INR10665 (£112.30) per case treated/prevented. A total of 98% of participants found the intervention acceptable.
Conclusions:
A virtual assistance-based lifestyle intervention was effective, cost-effective and acceptable in reducing risk factors for diabetes in young employees in the information technology industry, and is potentially scalable. This article is protected by copyright. All rights reserved.
A systematic review of randomized controlled trials was conducted to evaluate the effectiveness of eHealth interventions for the prevention and treatment of overweight and obesity in adults. Eight databases were searched for studies published in English from 1995 to 17 September 2014. Eighty-four studies were included, with 183 intervention arms, of which 76% (n = 139) included an eHealth component. Sixty-one studies had the primary aim of weight loss, 10 weight loss maintenance, eight weight gain prevention, and five weight loss and maintenance. eHealth interventions were predominantly delivered using the Internet, but also email, text messages, monitoring devices, mobile applications, computer programs, podcasts and personal digital assistants. Forty percent (n = 55) of interventions used more than one type of technology, and 43.2% (n = 60) were delivered solely using eHealth technologies. Meta-analyses demonstrated significantly greater weight loss (kg) in eHealth weight loss interventions compared with control (MD -2.70 [-3.33,-2.08], P < 0.001) or minimal interventions (MD -1.40 [-1.98,-0.82], P < 0.001), and in eHealth weight loss interventions with extra components or technologies (MD 1.46 [0.80, 2.13], P < 0.001) compared with standard eHealth programmes. The findings support the use of eHealth interventions as a treatment option for obesity, but there is insufficient evidence for the effectiveness of eHealth interventions for weight loss maintenance or weight gain prevention.
© 2015 World Obesity.
During the first 7 years of the Diabetes Prevention Program Outcomes Study (DPPOS), diabetes incidence rates when compared to DPP decreased in the placebo (PLB) (-42%) and metformin (MET) (-25%) groups compared to the rates in the intensive lifestyle (ILS) (+31%) group. Participants in PLB and MET groups were offered group ILS prior to starting DPPOS. Two hypotheses were explored to explain rate differences: 'effective intervention' (changes in weight and other factors due to ILS) and 'exhaustion of susceptibles' (changes in mean genetic and diabetes risk scores).No combination of behavioral risk factors (weight, physical activity, diet, smoking, antidepressant or statin use) explained the lower DPPOS rates of diabetes progression in PLB and MET whereas weight gain was the factor associated with higher rates in ILS. Different patterns in the average genetic risk score over time were consistent with 'exhaustion of susceptibles'.Results were consistent with 'exhaustion of susceptibles' for the change in incidence rates, but not the availability of ILS to all persons before the beginning of DPPOS. Thus, 'effective intervention' did not explain the lower diabetes rates in DPPOS among PLB and MET groups compared with DPP. ClinicalTrials.Gov: DPP - NCT00004992; DPPOS - NCT00038727.
Obesity is an increasing drain on the resources of general practitioners, who have few effective options for treatment other than surgery and (often prohibitively expensive) personal dietician advice. This pilot project investigated the weight loss efficacy and the cost of an interactive internet-based weight loss program in a Danish medical center setting. The study comprised an initial weight loss period of approximately 4 months, consisting of frequent online consultations with a dietician and an exercise coach supported by electronic diaries and establishment of an online community, where the patients exchanged experiences with other users of the program. This was followed by a 16-month maintenance treatment providing less intensive counseling. Of 46 obese patients offered participation, 32 patients were enrolled in the study and 21 completed the full course. The mean weight at inclusion was 104 kg with a BMI of 36.4 kg/m 2 . After 4 months of treatment and an average of 17 consultations the participants lost on average 7.0 kg, p<0.001. During the 16-month maintenance period, the average weight did not change and 81% of the participants retained or increased their initial weight loss. The cost of the initial treatment was calculated as 165 DKK (approx. €22) per kg weight lost. These results indicate that e-mail consultations can produce comparable weight loss as conventional weight loss treatments in general practice at a lower cost, particularly for sustaining the weight loss over a longer period of time. The results of this preliminary uncontrolled study with few participants indicate that future randomized clinical trials with more participants comparing the e-consultations with relevant conventional practices are justified, in order to quantify effect and long-term cost-efficiency of e-consultations as an intervention against obesity.
Hemoglobin A1c (HbA1c) has been accepted as an index of glycemic control since the mid-1970s and is the best marker for diabetic microvascular complications. Clinically, it is now used to assess glycemic control in people with diabetes. Assays are most reliable when certified by the National Hemoglobin Standardization Program but are subject to confounders and effect modifiers, particularly in the setting of hematologic abnormalities. Other measures of chronic glycemic control-fructosamine and 1,5-anhydroglucitol-are far less widely used. The relationship of HbA1c to average blood glucose was intensively studied recently, and it has been proposed that this conversion can be used to report an "estimated average glucose, eAG" in milligrams/deciliter or millimolar units rather than as per cent glycated hemoglobin. Finally, HbA1c has been proposed as a useful method of screening for and diagnosing diabetes.