Evaluation of the Clinical and Economic Effects of a Primary Care Anchored, Collaborative, Electronic Health Lifestyle Coaching Program in Denmark: Protocol for a Two-Year Randomized Controlled Trial

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity

BACKGROUND

Obesity is considered to be a risk factor for various diseases, and its incidence has tripled worldwide since 1975. In addition to potentially being at risk for adverse health outcomes, people with overweight or obesity are often stigmatised. Behaviour change interventions are increasingly delivered as mobile health (m-health) interventions, using smartphone apps and wearables. They are believed to support healthy behaviours at the individual level in a low-threshold manner.

OBJECTIVES

To assess the effects of integrated smartphone applications for adolescents and adults with overweight or obesity.

SEARCH METHODS

We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, and LILACS, as well as the trials registers ClinicalTrials.gov and World Health Organization International Clinical Trials Registry Platform on 2 October 2023 (date of last search for all databases). We placed no restrictions on the language of publication.

SELECTION CRITERIA

Participants were adolescents and adults with overweight or obesity. Eligible interventions were integrated smartphone apps using at least two behaviour change techniques. The intervention could target physical activity, cardiorespiratory fitness, weight loss, healthy diet, or self-efficacy. Comparators included no or minimal intervention (NMI), a different smartphone app, personal coaching, or usual care. Eligible studies were randomised controlled trials of any duration with a follow-up of at least three months.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methodology and the RoB 2 tool. Important outcomes were physical activity, body mass index (BMI) and weight, health-related quality of life, self-efficacy, well-being, change in dietary behaviour, and adverse events. We focused on presenting studies with medium- (6 to < 12 months) and long-term (≥ 12 months) outcomes in our summary of findings table, following recommendations in the core outcome set for behavioural weight management interventions.

MAIN RESULTS

We included 18 studies with 2703 participants. Interventions lasted from 2 to 24 months. The mean BMI in adults ranged from 27 to 50, and the median BMI z-score in adolescents ranged from 2.2 to 2.5. Smartphone app versus no or minimal intervention Thirteen studies compared a smartphone app versus NMI in adults; no studies were available for adolescents. The comparator comprised minimal health advice, handouts, food diaries, smartphone apps unrelated to weight loss, and waiting list. Measures of physical activity: at 12 months' follow-up, a smartphone app compared to NMI probably reduces moderate to vigorous physical activity (MVPA) slightly (mean difference (MD) -28.9 min/week (95% confidence interval (CI) -85.9 to 28; 1 study, 650 participants; moderate-certainty evidence)). We are very uncertain about the results of estimated energy expenditure and cardiorespiratory fitness at eight months' follow-up. A smartphone app compared with NMI probably results in little to no difference in changes in total activity time at 12 months' follow-up and leisure time physical activity at 24 months' follow-up. Anthropometric measures: a smartphone app compared with NMI may reduce BMI (MD of BMI change -2.6 kg/m2, 95% CI -6 to 0.8; 2 studies, 146 participants; very low-certainty evidence) at six to eight months' follow-up, but the evidence is very uncertain. At 12 months' follow-up, a smartphone app probably resulted in little to no difference in BMI change (MD -0.1 kg/m2, 95% CI -0.4 to 0.3; 1 study; 650 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in body weight change (MD -2.5 kg, 95% CI -6.8 to 1.7; 3 studies, 1044 participants; low-certainty evidence) at 12 months' follow-up. At 24 months' follow-up, a smartphone app probably resulted in little to no difference in body weight change (MD 0.7 kg, 95% CI -1.2 to 2.6; 1 study, 245 participants; moderate-certainty evidence). A smartphone app compared with NMI may result in little to no difference in self-efficacy for a physical activity score at eight months' follow-up, but the results are very uncertain. A smartphone app probably results in little to no difference in quality of life and well-being at 12 months (moderate-certainty evidence) and in little to no difference in various measures used to inform dietary behaviour at 12 and 24 months' follow-up. We are very uncertain about adverse events, which were only reported narratively in two studies (very low-certainty evidence). Smartphone app versus another smartphone app Two studies compared different versions of the same app in adults, showing no or minimal differences in outcomes. One study in adults compared two different apps (calorie counting versus ketogenic diet) and suggested a slight reduction in body weight at six months in favour of the ketogenic diet app. No studies were available for adolescents. Smartphone app versus personal coaching Only one study compared a smartphone app with personal coaching in adults, presenting data at three months. Two studies compared these interventions in adolescents. A smartphone app resulted in little to no difference in BMI z-score compared to personal coaching at six months' follow-up (MD 0, 95% CI -0.2 to 0.2; 1 study; 107 participants). Smartphone app versus usual care Only one study compared an app with usual care in adults but only reported data at three months on participant satisfaction. No studies were available for adolescents. We identified 34 ongoing studies.

AUTHORS' CONCLUSIONS

The available evidence is limited and does not demonstrate a clear benefit of smartphone applications as interventions for adolescents or adults with overweight or obesity. While the number of studies is growing, the evidence remains incomplete due to the high variability of the apps' features, content and components, which complicates direct comparisons and assessment of their effectiveness. Comparisons with either no or minimal intervention or personal coaching show minor effects, which are mostly not clinically significant. Minimal data for adolescents also warrants further research. Evidence is also scarce for low- and middle-income countries as well as for people with different socio-economic and cultural backgrounds. The 34 ongoing studies suggest sustained interest in the topic, with new evidence expected to emerge within the next two years. In practice, clinicians and healthcare practitioners should carefully consider the potential benefits, limitations, and evolving research when recommending smartphone apps to adolescents and adults with overweight or obesity.

Motivation for Behavior Change among Women with Recent Gestational Diabetes and Their Partners-A Qualitative Investigation among Participants in the Face-It Intervention

Promoting diet and physical activity is important for women with recent gestational diabetes mellitus (GDM) and their partners to reduce the risk of future type 2 diabetes (T2D). The study aimed to understand how motivation for changing diet and physical activity behaviors among women with recent GDM and their partners was experienced after participation in the Danish Face-it intervention. Fourteen couples' interviews were conducted. Data analysis followed a reflexive thematic analysis. Guided by self-determination theory and interdependence theory, we identified four themes affecting couples' motivation for health behavior change: (1) The need to feel understood after delivery; (2) adjusting health expectations; (3) individual and mutual preferences for health behaviors; and (4) the health threat of future T2D as a cue to action. We found that couples in general perceived the Face-it intervention as useful and motivating. Using couple interviews increased our understanding of how the women and partners influenced each other's perspectives after a GDM-affected pregnancy and thus how targeting couples as opposed to women alone may motivate health behavior change.

The Usability of Homelab, a Digital Self-service at a Dutch General Practice, for Diagnostic Tests: Pilot Study With a Questionnaire

BACKGROUND

eHealth potentially can make health care more accessible and efficient and help reduce the workload in primary health care. Homelab is an eHealth tool implemented in a general practice environment, and it offers relatively simple laboratory diagnostic tests without the referral of the general practitioner. After logging in this eHealth tool, patients select and order a diagnostic test based on their symptoms. The test results are presented online to the general practitioner and the patient.

OBJECTIVE

This study aims to evaluate the use, usability, and user characteristics of Homelab. Further, it aims to evaluate whether Homelab replaces an appointment with the general practitioner.

METHODS

Homelab has been implemented since May 2021 as a pilot in a Dutch general practice. The number of requests and the ordered diagnostic packages are monitored. After using Homelab, patients are invited to complete a short questionnaire. The questionnaire contains demographic questions and assesses usability using the System Usability Scale (10 items). In addition, questions about requesting an appointment with the general practitioner without Homelab are included. All data were anonymous.

RESULTS

The questionnaire was filled by 74 individual patients. The mean age of the patients was 40.33 (SD 12.11) years, and half of them were females (39/74, 53%). The majority of the patients were highly educated (56/74, 76%) and employed (53/74, 72%). Approximately 81% (60/74) of the patients reported that they would use Homelab again in the future and 66% (49/74) reported that they would have gone to the general practitioner if they had not used Homelab. The usability of Homelab was perceived higher by the younger age group (mean 73.96, SD 14.74) than by the older age group (mean 61.59, SD 14.37). In total, 106 test packages were ordered over 1 year, and the most requested diagnostic package was "Am I still healthy? I want to do my annual health checkup." Homelab was used the most during the months of the COVID-19 lockdown.

CONCLUSIONS

The use of Homelab, a digital self-service for ordering diagnostic tests, was monitored in this study, and its usability was perceived as above average. Our findings showed that patients are willing to use Homelab in the future and they would use it most of the time as a replacement for regular consultations. Homelab offers opportunities for more accessible and efficient health care for both the patient and the general practitioner.

Long-term weight loss in a 24-month primary care-anchored telehealth lifestyle coaching program: Randomized controlled trial

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.

Agreement Between Clinically Measured Weight and Self-reported Weight Among Patients With Type 2 Diabetes Through an mHealth Lifestyle Coaching Program in Denmark: Secondary Analysis of a Randomized Controlled Trial

Background

Digital health interventions are increasingly used to handle and promote positive health behaviors. Clinical measures are often used, and a certain precision is essential for digital health interventions to have an effect. Only few studies have compared clinically measured weights with self-reported weights. No study has examined the validity of self-reported weight from a mobile app used in a tailored weight loss intervention.

Objective

The aim of this study was to analyze the agreement between clinically measured weight and self-reported weight collected from a mobile health lifestyle coaching program during a 12-month weight loss intervention for obese patients with and without type 2 diabetes. The secondary aim was to investigate the determinants for possible discrepancies between clinically measured and self-reported weights of these patients with different demographic and lifestyle characteristics and achievements of weight loss goals.

Methods

Weight registrations were collected from participants (N=104) in a Danish randomized controlled trial examining the effect of a digital lifestyle intervention on weight loss among obese patients with and without type 2 diabetes. Data were collected at baseline and after 6 and 12 months. Self-reported weight was measured at home and registered in the app.

Results

Self-reported body weight was lower than the weight measured in the clinic after 6 months by 1.03 kg (95% CI 1.01-1.05; P<.001) and after 12 months also by 1.03 kg (95% CI 0.99-1.04; P<.001). After 6 months, baseline weight and BMI were associated with a discrepancy of 0.03 kg (95% CI 0.01-0.04; P=.01) and 0.09 kg (95% CI 0.02-0.17; P=.02) per increment of 1 kg and 1 kg/m2, respectively, between clinically measured weight and self-reported weight. Weight change during the first 6 months was also associated with a difference of 0.1 kg (95% CI 0.04-0.01; P<.001) per kilogram of difference in weight between clinically measured weight and self-reported weight. Participants who did not achieve the 5% weight loss goal underestimated their weight by 0.79 kg (95% CI 0.34-1.23) at 6 months. After 12 months, only baseline weight was associated with a discrepancy of 0.03 kg (95% CI 0.01-0.05; P=.02) per increment of kilogram between clinically measured weight and self-reported weight. None of the other factors showed any significant discrepancy after 12 months.

Conclusions

Self-reported weight obtained from mobile health is a valid method for collecting anthropometric measurements.

Trial Registration

ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915

Reversing Type 2 Diabetes in a Primary Care-Anchored eHealth Lifestyle Coaching Programme in Denmark: A Randomised Controlled Trial

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.

Long term Weight Loss in a Primary Care-Anchored eHealth Lifestyle Coaching Program in Denmark: Randomized Controlled Trial (Preprint)

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 A_1c 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

Preferred Content in Occupational Therapy-Based Weight Loss Interventions: A Survey

Despite decades of attempts from doctors and dietitians to break the obesity curve, obesity rates are increasing in Denmark. To counteract this, municipal occupational therapy-based interventions are in development as part of the Danish Obesity Intervention Trial (DO: IT). The objective was to survey Danish adults on preferred content in an occupational therapy municipal weight loss intervention and determine if preferences differed by gender and age. A 21-item questionnaire was developed to explore preferences related to diet, physical activity, occupational balance, habits, and social relations. In all, 429 respondents were included. Chi-square statistics were performed to determine if preferences differed by gender and age. A total of 15 of the 21 items indicated gender-based differences in preferences. Two items indicated age-based differences. Notably, compared to men, women indicated significantly more interest in content relating to habit formation and occupational balance. Gender-based differences should be considered when developing a weight loss intervention.

Systematically developing a family-based health promotion intervention for women with prior gestational diabetes based on evidence, theory and co-production: the Face-it study

BACKGROUND

Women with prior gestational diabetes mellitus (GDM) are at high risk of developing type 2 diabetes; however, this risk can be reduced by engaging in positive health behaviours e.g. healthy diet and regular physical activity. As such behaviours are difficult to obtain and maintain there is a need to develop sustainable behavioural interventions following GDM. We aimed to report the process of systematically developing a health promotion intervention to increase quality of life and reduce diabetes risk among women with prior GDM and their families. We distil general lessons about developing complex interventions through co-production and discuss our extensions to intervention development frameworks.

METHODS

The development process draws on the Medical Research Council UK Development of complex interventions in primary care framework and an adaptation of a three-stage framework proposed by Hawkins et al. From May 2017 to May 2019, we iteratively developed the Face-it intervention in four stages: 1) Evidence review, qualitative research and stakeholder consultations; 2) Co-production of the intervention content; 3) Prototyping, feasibility- and pilot-testing and 4) Core outcome development. In all stages, we involved stakeholders from three study sites.

RESULTS

During stage 1, we identified the target areas for health promotion in families where the mother had prior GDM, including applying a broad understanding of health and a multilevel and multi-determinant approach. We pinpointed municipal health visitors as deliverers and the potential of using digital technology. In stage 2, we tested intervention content and delivery methods. A health pedagogic dialogue tool and a digital health app were co-adapted as the main intervention components. In stage 3, the intervention content and delivery were further adapted in the local context of the three study sites. Suggestions for intervention manuals were refined to optimise flexibility, delivery, sequencing of activities and from this, specific training manuals were developed. Finally, at stage 4, all stakeholders were involved in developing realistic and relevant evaluation outcomes.

CONCLUSIONS

This comprehensive description of the development of the Face-it intervention provides an example of how to co-produce and prototype a complex intervention balancing evidence and local conditions. The thorough, four-stage development is expected to create ownership and feasibility among intervention participants, deliverers and local stakeholders.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03997773 , registered retrospectively on 25 June 2019.

Identification of Important Factors Affecting Use of Digital Individualised Coaching and Treatment of Type 2 Diabetes in General Practice: A Qualitative Feasibility Study

Most type 2 diabetes patients are treated in general practice and there is a need of developing and implementing efficient lifestyle interventions. eHealth interventions have shown to be effective in promoting a healthy lifestyle. The purpose of this study was to test the feasibility, including the identification of factors of importance, when offering digital lifestyle coaching to type 2 diabetes patients in general practice. We conducted a qualitative feasibility study with focus group interviews in four general practices. We identified two overall themes and four subthemes: (1) the distribution of roles and lifestyle interventions in general practice (subthemes: external and internal distribution of roles) and (2) the pros and cons for digital lifestyle interventions in general practice (subthemes: access to real life data and change in daily routines). We conclude that for digital lifestyle coaching to be feasible in a general practice setting, it was of great importance that the general practitioners and practice nurses knew the role and content of the intervention. In general, there was a positive attitude in the general practice setting towards referring type 2 diabetes patients to digital lifestyle intervention if it was easy to refer the patients and if easily understandable and accessible feedback was implemented into the electronic health record. It was important that the digital lifestyle intervention was flexible and offered healthcare providers in general practice an opportunity to follow the type 2 diabetes patient closely.