Investigating when, which and why users stop using an e- and m-health intervention to promote an active lifestyle: A focus on HAPA-based psychosocial determinants. (Preprint)

2021 ◽  
Author(s):  
Helene Schroé ◽  
Geert Crombez ◽  
Ilse De Bourdeaudhuij ◽  
Delfien Van Dyck
Keyword(s):  
Behaviour Change ◽  
Demographic Variables ◽  
Health Intervention ◽  
Health Interventions ◽  
Descriptive Statistics ◽  
Drop Out ◽  
Attrition Rates ◽  
Technical Problems ◽  
Psychosocial Determinants ◽  
In The Beginning

BACKGROUND E- and m-health interventions have gained momentum to change health behaviours such as physical activity (PA) and sedentary behaviour (SB). Although these interventions show promising results in terms of behaviour change, they still suffer from high attrition rates, resulting in a lower potential and reachability. In order to reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using e- and m-health interventions. Certain demographic variables have already been related to attrition in e- and m-health interventions, however the role of psychosocial determinants of behaviour change as predictors of attrition has not yet been fully explored. OBJECTIVE The aim of this study was to examine when, which and why users stop using an e- and m-health intervention. In particular, we aimed to investigate whether psychosocial determinants of behaviour change were predictors for attrition. METHODS The sample consisted of 473 healthy adults who participated in the e-and m-health intervention ‘MyPlan 2.0’ to promote PA or reduce SB. The intervention was developed using the Health Action Process Approach (HAPA) model, which describes psychosocial determinants that guide individuals in changing their behaviour. If participants stopped with the intervention, a questionnaire with eight question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and post-test measurements, and 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioural status and HAPA-based psychosocial determinants at pre-test measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of answers to the attrition related questionnaire were used. RESULTS The study demonstrated that 227 of the 473 (47,9%) participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participants’ scores on the psychosocial determinants action planning, coping planning and self-monitoring were predictors of first session, third session and/or whole intervention completion. The most endorsed reasons to stop with the intervention were the time-consuming nature of questionnaires, not having time, dissatisfaction with the content of the intervention, technical problems, already meeting the guidelines for PA/SB, and to a lesser extent the experience of medical/emotional problems. CONCLUSIONS This study provides some directions for future studies. To decrease attrition, it will be important to personalise interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by for example using objective monitoring devices, and technical aspects of e-and m-health interventions should be thoroughly tested in advance.

scholarly journals Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

2020 ◽  
Author(s):  
Helene Schroé ◽  
Delfien Van Dyck ◽  
Annick De Paepe ◽  
Louise Poppe ◽  
Wen Wei Loh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Behaviour Change ◽  
Action Planning ◽  
Health Intervention ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Self Monitoring ◽  
Coping Planning ◽  
The Impact

Abstract BackgroundE- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB).MethodsIn a 2(action planning: present vs absent) x2(coping planning: present vs absent) x2(self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335,age = 35.8,28.1% men) or SB (n = 138,age = 37.8,37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB.ResultsFirst, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735,p = 0.007) and reduced SB (t=-2.573,p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302,p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8,849,p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918,p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590,p = 0.014;x2 = 17.722,p < 0.001;x2 = 4.552,p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389,p = 0.031) and self-monitoring alone (x2 = 8.858,p = 003), respectively.ConclusionsThis study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registrationThis study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017, http://clinicaltrials.gov/ct2/show/NCT03274271

scholarly journals Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

2020 ◽  
Author(s):  
Helene Schroé ◽  
Delfien Van Dyck ◽  
Annick De Paepe ◽  
Louise Poppe ◽  
Wen Wei Loh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Behaviour Change ◽  
Action Planning ◽  
Health Intervention ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Self Monitoring ◽  
Coping Planning ◽  
The Impact

Abstract Background E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs.Methods In a 2 (action planning: present vs absent) x2 (coping planning: present vs absent) x2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n=335,age=35.8,28.1% men) or SB (n=138,age=37.8,37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. Results First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t=2.735,p=0.007) and reduced SB (t=-2.573,p=0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t=2.302,p=0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2=8,849,p=0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2=3.918,p=0.048). To increase PA, action planning was always more effective in combination with coping planning (x2=5.590,p=0.014;x2=17.722,p<0.001;x2=4.552,p=0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2=4.389,p=0.031) and self-monitoring alone (x2=8.858,p=003), respectively.Conclusions This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registration This study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017, http://clinicaltrials.gov/ct2/show/NCT03274271

scholarly journals Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Helene Schroé ◽  
Delfien Van Dyck ◽  
Annick De Paepe ◽  
Louise Poppe ◽  
Wen Wei Loh ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behaviour ◽  
Behaviour Change ◽  
Action Planning ◽  
Health Intervention ◽  
Health Interventions ◽  
Behaviour Change Techniques ◽  
Self Monitoring ◽  
Coping Planning ◽  
The Impact

Abstract Background E- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs. Methods In a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB. Results First, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = − 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively. Conclusions This study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future. Trial registration This study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017.

scholarly journals Cheating on exams: Investigating Reasons, Attitudes, and the Role of Demographic Variables

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110041
Author(s):  
Mohammad Salehi ◽  
Samaneh Gholampour
Keyword(s):  
Negative Attitude ◽  
Demographic Variables ◽  
Descriptive Statistics ◽  
Achievement Scores ◽  
Inferential Statistics ◽  
Cheating Behavior ◽  
Iranian Students ◽  
Iranian Context ◽  
Relationship Of

Cheating is an academically dishonest behavior about which there has been a thrust of research. However, it has not been extensively researched in an Iranian context. Therefore, the current study was conducted with 310 Iranian students. A cheating questionnaire was devised and administered to the participants. Certain demographic variables were investigated. Both descriptive and inferential statistics were employed to analyze the obtained data. The results of the descriptive statistics revealed that cheating was common among participants, and most students did not harbor any negative attitude toward cheating or at least were neutral about it. The most common method of cheating was “letting others look at their papers while taking exams.” The most common reason for cheating was “not being ready for the exam.” As for inferential statistics, one-way analysis of variance, an independent t-test, and correlational analyses were used to test the effect and relationship of demographic variables on and between the cheating behaviors of the participants. It was found that none of the two demographic variables of gender and year level had any effect on students’ cheating behaviors. Furthermore, achievement scores and age were not significantly correlated with cheating behavior scores.

A systematic review of mobile health interventions in China: Identifying gaps in care

2019 ◽  
pp. 1357633X1985674 ◽  
Author(s):  
Xiaoshi Yang ◽  
Carrie L Kovarik
Keyword(s):  
Systematic Review ◽  
Behaviour Change ◽  
Mobile Health ◽  
Urban Areas ◽  
Healthcare Services ◽  
Text Messages ◽  
Health Interventions ◽  
Health System Strengthening ◽  
World Health ◽  
Common Disease

Introduction Mobile health has a promising future in the healthcare system in most developed countries. China’s rapidly developing mobile technology infrastructure offers an unprecedented opportunity for wide adoption of mobile health interventions in the delivery of effective and timely healthcare services. However, there is little data on the current extent of the mobile health landscape in China. The aim of this study was to systematically review the existing mobile health initiatives in China, characterise the technology used, disease categories targeted, location of the end user (urban versus rural), and examine the potential effects of mobile health on health system strengthening in China. Furthermore, we identified gaps in development and evaluation of the effectiveness of mobile health interventions. Methods A systematic review of the literature published from 18 December 2015–3 April 2019 was conducted and yielded 2863 articles from English and Chinese retrieval database and trial registries, including PubMed, EMBASE, China National Knowledge of Infrastructure and World Health Organization International Clinical Trials Registry Platform. Studies were included if they used mobile health to support patient healthcare outcomes. Results A total of 1129 full-text articles were assessed and 338 were included in this study. The review found that most studies targeted client education and behaviour change via applications (apps) (65.4%), including WeChat, and text messaging (short text messages) (19.8%) to improve patient medical treatment outcomes such as compliance and appointment reminders. The most common disease-specific mobile health interventions focused primarily on chronic disease management and behaviour change in cardiology (13.3%), endocrinology/diabetes (12.1%), behavioural health (11.8%), oncology (11.2%) and neurology (6.8%). The mobile health interventions related to nutrition (0.6%) and chronic respiratory diseases (1.6%) are underrepresented in mobile health in comparison to the burden of disease in China. The majority (90.0%) of the mobile health interventions were conducted exclusively in urban areas, with few opportunities reaching rural populations. Conclusions Overall, mobile health has a promising future in China, with recent rapid growth in initiatives. The majority are focused on education and behaviour change in the realm of chronic diseases and target patients in urban areas. The imbalance in mobile health between the urban and rural areas, as well as between population disease spectrum and health service delivery, pose substantial dilemmas. However, mobile health may be redirected to correct this imbalance, possibly improving access to healthcare services, and filling the gaps in order to improve health equity for the underserved populations in China.

scholarly journals Employees’ Perspectives on the Facilitators and Barriers to Engaging With Digital Mental Health Interventions in the Workplace: Qualitative Study

2018 ◽  
Vol 5 (1) ◽  
pp. e8 ◽  
Author(s):  
Stephany Carolan ◽  
Richard O de Visser
Keyword(s):  
Mental Health ◽  
Controlled Trial ◽  
Health Intervention ◽  
Health Interventions ◽  
Mental Health Intervention ◽  
Depression And Anxiety ◽  
Occupational Setting ◽  
Mental Health Interventions ◽  
Facilitators And Barriers ◽  
Workplace Setting

Background Prevalence rates of work-related stress, depression, and anxiety are high, resulting in reduced productivity and increased absenteeism. There is evidence that these conditions can be successfully treated in the workplace, but take-up of psychological treatments among workers is low. Digital mental health interventions delivered in the workplace may be one way to address this imbalance, but although there is evidence that digital mental health is effective at treating stress, depression, and anxiety in the workplace, uptake of and engagement with these interventions remains a concern. Additionally, there is little research on the appropriateness of the workplace for delivering these interventions or on what the facilitators and barriers to engagement with digital mental health interventions in an occupational setting might be. Objective The aim of this research was to get a better understanding of the facilitators and barriers to engaging with digital mental health interventions in the workplace. Methods Semistructured interviews were held with 18 participants who had access to an occupational digital mental health intervention as part of a randomized controlled trial. The interviews were transcribed, and thematic analysis was used to develop an understanding of the data. Results Digital mental health interventions were described by interviewees as convenient, flexible, and anonymous; these attributes were seen as being both facilitators and barriers to engagement in a workplace setting. Convenience and flexibility could increase the opportunities to engage with digital mental health, but in a workplace setting they could also result in difficulty in prioritizing time and ensuring a temporal and spatial separation between work and therapy. The anonymity of the Internet could encourage use, but that benefit may be lost for people who work in open-plan offices. Other facilitators to engagement included interactive and interesting content and design features such as progress trackers and reminders to log in. The main barrier to engagement was the lack of time. The perfect digital mental health intervention was described as a website that combined a short interactive course that was accessed alongside time-unlimited information and advice that was regularly updated and could be dipped in and out of. Participants also wanted access to e-coaching support. Conclusions Occupational digital mental health interventions may have an important role in delivering health care support to employees. Although the advantages of digital mental health interventions are clear, they do not always fully translate to interventions delivered in an occupational setting and further work is required to identify ways of minimizing potential barriers to access and engagement. Trial Registration ClinicalTrials.gov: NCT02729987; https://clinicaltrials.gov/ct2/show/NCT02729987?term=NCT02729987& rank=1 (Archived at WebCite at http://www.webcitation.org/6wZJge9rt)

scholarly journals Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Vincent Bremer ◽  
Philip I Chow ◽  
Burkhardt Funk ◽  
Frances P Thorndike ◽  
Lee M Ritterband
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Behavioral Therapy ◽  
Digital Health ◽  
Area Under The Curve ◽  
Prediction Performance ◽  
Health Interventions ◽  
Drop Out ◽  
Support Vector ◽  
Boosted Decision Trees

BACKGROUND User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. OBJECTIVE The purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. METHODS Steps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. RESULTS Accuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. CONCLUSIONS The results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.

scholarly journals Employees� Perspectives on the Facilitators and Barriers to Engaging With Digital Mental Health Interventions in the Workplace: Qualitative Study (Preprint)

2017 ◽  
Author(s):  
Stephany Carolan ◽  
Richard O de Visser
Keyword(s):  
Mental Health ◽  
Controlled Trial ◽  
Health Intervention ◽  
Health Interventions ◽  
Mental Health Intervention ◽  
Depression And Anxiety ◽  
Occupational Setting ◽  
Mental Health Interventions ◽  
Facilitators And Barriers ◽  
Workplace Setting

BACKGROUND Prevalence rates of work-related stress, depression, and anxiety are high, resulting in reduced productivity and increased absenteeism. There is evidence that these conditions can be successfully treated in the workplace, but take-up of psychological treatments among workers is low. Digital mental health interventions delivered in the workplace may be one way to address this imbalance, but although there is evidence that digital mental health is effective at treating stress, depression, and anxiety in the workplace, uptake of and engagement with these interventions remains a concern. Additionally, there is little research on the appropriateness of the workplace for delivering these interventions or on what the facilitators and barriers to engagement with digital mental health interventions in an occupational setting might be. OBJECTIVE The aim of this research was to get a better understanding of the facilitators and barriers to engaging with digital mental health interventions in the workplace. METHODS Semistructured interviews were held with 18 participants who had access to an occupational digital mental health intervention as part of a randomized controlled trial. The interviews were transcribed, and thematic analysis was used to develop an understanding of the data. RESULTS Digital mental health interventions were described by interviewees as convenient, flexible, and anonymous; these attributes were seen as being both facilitators and barriers to engagement in a workplace setting. Convenience and flexibility could increase the opportunities to engage with digital mental health, but in a workplace setting they could also result in difficulty in prioritizing time and ensuring a temporal and spatial separation between work and therapy. The anonymity of the Internet could encourage use, but that benefit may be lost for people who work in open-plan offices. Other facilitators to engagement included interactive and interesting content and design features such as progress trackers and reminders to log in. The main barrier to engagement was the lack of time. The perfect digital mental health intervention was described as a website that combined a short interactive course that was accessed alongside time-unlimited information and advice that was regularly updated and could be dipped in and out of. Participants also wanted access to e-coaching support. CONCLUSIONS Occupational digital mental health interventions may have an important role in delivering health care support to employees. Although the advantages of digital mental health interventions are clear, they do not always fully translate to interventions delivered in an occupational setting and further work is required to identify ways of minimizing potential barriers to access and engagement. CLINICALTRIAL ClinicalTrials.gov: NCT02729987; https://clinicaltrials.gov/ct2/show/NCT02729987?term=NCT02729987& rank=1 (Archived at WebCite at http://www.webcitation.org/6wZJge9rt)

Are SSRIs a Cost-Effective Alternative to Tricyclics?

1996 ◽  
Vol 168 (4) ◽  
pp. 404-409 ◽  
Author(s):  
Matthew Hotopf ◽  
Glyn Lewis ◽  
Charles Normand
Keyword(s):  
Cost Effectiveness ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Cost Effective ◽  
Drop Out ◽  
Attrition Rates ◽  
The Difference ◽  
Randomised Controlled ◽  
Meta Analyses ◽  
The Cost ◽  
Effectiveness Studies

BackgroundSelective serotonin reuptake inhibitors (SSRIs) are more expensive than tricyclics. Reports have suggested that SSRIs are cost-effective because they are better tolerated and safer in overdose.MethodA systematic review of all randomised controlled trials (RCTs), meta-analyses, and cost-effectiveness studies comparing SSRIs and tricyclic antidepressants (TCAs).ResultsNone of the RCTs provided an economic analysis and there were methodological problems in the majority which would preclude this approach. Meta-analyses suggest that clinical efficacy is equivalent but slightly fewer patients prescribed SSRIs drop out of RCTs. Cost-effectiveness studies have been based on crude ‘modelling’ approaches and over-estimate the difference in attrition rates and the cost of treatment failure. It appears impossible to evaluate the economic aspects of suicide because of its rarity.ConclusionsThere is no evidence to suggest that SSRIs are more cost-effective than TCAs. The debate will only be concluded when a prospective cost-effectiveness study is done in the setting of a large primary care based RCT.

Systematic review and meta-analysis of attrition within digital health interventions for people with multiple sclerosis (Preprint)

2021 ◽  
Author(s):  
William Bevens
Keyword(s):  
Multiple Sclerosis ◽  
Systematic Review ◽  
Digital Health ◽  
Meta Analysis ◽  
Health Interventions ◽  
Attrition Rates ◽  
Participant Engagement ◽  
Randomised Controlled ◽  
And Control ◽  
Insight Into

BACKGROUND Digital health interventions (DHI) have revolutionised the management of multiple sclerosis (MS). It is now understood that the technological elements that comprise DHIs can influence participant engagement and that people with MS (PwMS) can experience significant barriers to remaining enrolled in DHIs related to the use of these elements. It is essential to explore the influence of technological elements in mitigating attrition after allocation. OBJECTIVE We examined the study design and technological elements of documented DHIs targeted at PwMS and how these correlated with attrition among participants of randomised-controlled trials (RCTs). METHODS We conducted a systematic review and meta-analysis of RCTs (n=17) describing digital technologies for health interventions for PwMS. We analysed attrition of included studies using a random-effects model and meta-regression to measure the association between potential moderators. RESULTS There were no measured differences in attrition between intervention and control arms; however, some of the heterogeneity observed was explained by the composite technological element score. The pooled attrition rates for the intervention and control arms were 10.6% and 11.2% respectively. CONCLUSIONS Ultimately, this paper provides insight into the technological composition of DHIs and will aid in the design of future studies in this area.

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