Treatment Satisfaction in a Randomized Clinical Trial of mHealth Smoking Abstinence Reinforcement

Mobile Health Interventions and RCTs: Structured Taxonomy and Research Framework

Mobile Health Interventions (MHIs) have addressed a range of healthcare challenges and have been evaluated using Randomized Controlled Trials (RCTs) to establish clinical effectiveness. Using PRISMA we conducted a systematic literature review of RCTs for MHIs and identified 70 studies which were analyzed and classified using Nickerson-Varshney-Muntermann (NVM) taxonomy. From the resultant iterations of the taxonomy, we extracted insights from the categorized studies. RCTs cover a wide range of health conditions including chronic diseases, general wellness, unhealthy practices, family planning, end-of-life, and post-transplant care. The MHIs that were utilized by the RCTs were varied as well, although most studies did not find significant differences between MHIs and usual care. The challenges for MHI-based RCTs include the use of technologies, delayed outcomes, patient recruitment, patient retention, and complex regulatory requirements. These variances can lead to a higher rate of Type I/Type II errors. Further considerations are the impact of infrastructure, contextual and cultural factors, and reductions in the technological relevancy of the intervention itself. Finally, due to the delayed effect of most outcomes, RCTs of insufficient duration are unable to measure significant, lasting improvements. Using the insights from seventy identified studies, we developed a classification of existing RCTs along with guidelines for MHI-based RCTs and a research framework for future RCTs. The framework offers opportunities for (a) personalization of MHIs, (b) use of richer technologies, and (c) emerging areas for RCTs.

Responsiveness to mHealth Intervention for Cannabis Use in Young Adults Predicts Improved Outcomes

Mobile health (mHealth) interventions have proliferated rapidly in part because of their advantages in reducing consumer and provider burden, but less attention has been paid to participant responsiveness to mHealth programs and how this may affect outcomes. This study adds to that literature by examining whether participant responsiveness to a text messaging-delivered intervention was predictive of treatment outcomes over baseline levels of the outcome. We analyzed data from a pilot-randomized controlled trial of a text messaging-intervention to treat young adults with cannabis use disorder (treatment arm, N = 47), examining three indicators of responsiveness (two behavioral: treatment completion and booster message participation; and one subjective: perceived helpfulness of treatment) on abstinence from cannabis use and use-related problems measured at 3-month follow-up. With the exception of completion, the indicators were positively correlated with each other. Each of the indicators was predictive of better treatment outcomes above and beyond baseline risk. Treatment completion and booster participation-measured via technical data captured during intervention administration-appeared to be stronger predictors of improved outcomes than self-reported perceived helpfulness. Results suggest that behavioral and subjective responsiveness measures appear to be valid indicators of treatment response to mHealth interventions for substance use. Responsiveness measured via technical data captured during intervention administration may be a stronger and more efficient strategy for monitoring continued engagement. We discuss implications of these findings for deploying mHealth interventions at scale and monitoring responsiveness.

The Box: Methods and Results of a Real World Experience of mHealth Implementation in Clinical Practice

Background

Mobile health (mHealth) is an emerging field of scientific interest worldwide. Potential benefits include increased patient engagement, improved clinical outcomes, and reduced health care costs. However, mHealth is often studied in projects or trials, and structural implantation in clinical practice is less common.

Objective

The purpose of this paper is to outline the design of the Box and its implementation and use in an outpatient clinic setting. The impact on logistical outcomes and patient and provider satisfaction is discussed.

Methods

In 2016, an mHealth care track including smartphone-compatible devices, named the Box, was implemented in the cardiology department of a tertiary medical center in the Netherlands. Patients with myocardial infarction, rhythm disorders, cardiac surgery, heart failure, and congenital heart disease received devices to measure daily weight, blood pressure, heart rate, temperature, and oxygen saturation. In addition, professional and patient user comments on the experience with the care track were obtained via structured interviews.

Results

From 2016 to April 2020, a total of 1140 patients were connected to the mHealth care track. On average, a Box cost €350 (US $375), not including extra staff costs. The median patient age was 60.8 (IQR 52.9-69.3) years, and 73.59% (839/1140) were male. A median of 260 (IQR 105-641) measurements was taken on a median of 189 (IQR 98-372) days. Patients praised the ease of use of the devices and felt more involved with their illness and care. Professionals reported more productive outpatient consultations as well as improved insight into health parameters such as blood pressure and weight. A feedback loop from the hospital to patient to focus on measurements was commented as an important improvement by both patients and professionals.

Conclusions

In this study, the design and implementation of an mHealth care track for outpatient follow-up of patients with various cardiovascular diseases is described. Data from these 4 years indicate that mHealth is feasible to incorporate in outpatient management and is generally well-accepted by patients and providers. Limitations include the need for manual measurement data checks and the risk of data overload. Moreover, the tertiary care setting in which the Box was introduced may limit the external validity of logistical and financial end points to other medical centers. More evidence is needed to show the effects of mHealth on clinical outcomes and on cost-effectiveness.

Parents' Perceptions of the Acceptability of Evidence-Based Interventions to Support Transition From Neonatal to Rehabilitation Services

BACKGROUND

Parents of children born preterm with a disability often experience profound psychological distress with transition from neonatal to rehabilitation services. Four interventions were found effective to support parents throughout this critical transition period whereby parental stress can threaten the child's development.

PURPOSE

To examine parents' perceptions of the acceptability of four evidence-based interventions to support their transition.

METHODS

A quantitative design using survey methods was employed with 24 parents with experience in transition to rehabilitation services. Each participant rated the acceptability of the interventions using the Treatment Perception and Preference scale. Descriptive statistics and repeated measures analysis of variance were used for data analysis.

RESULTS

Mean overall acceptability scores differed across the four interventions (p = .042); the difference was of moderate size (η² = .11). Parents perceived psychoeducation and narrative therapy as most acceptable, followed by website consultation with healthcare providers and parent support program, then parent self-help program.

CONCLUSIONS

Psychoeducation and narrative therapy should be accessible to parents experiencing transition from neonatal to rehabilitation services.

A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker

Evidence of the long-term efficacy of digital therapies for smoking cessation that include a smartphone application (app) is limited. In this multi-center randomized controlled trial, we tested the efficacy of a novel digital therapy for smoking cessation: the "CureApp Smoking Cessation (CASC)" system, including a CASC smartphone app, a web-based patient management PC software for primary physicians, and a mobile exhaled carbon monoxide (CO) checker. A total of 584 participants with nicotine dependence were recruited from October 2017 to January 2018, and allocated 1:1 to the CASC intervention group or the control group. Both groups received a standard smoking cessation treatment with pharmacotherapy and counseling for 12 weeks. Meanwhile, the intervention group used the CASC system, and the control group used a control-app without a mobile CO checker, each for 24 weeks. The primary outcome was the biochemically validated continuous abstinence rate (CAR) from weeks 9 to 24. The main secondary outcome was an extended CAR from weeks 9 to 52. Except for 12 participants who did not download or use the apps, 285 participants were assigned to the intervention group, and 287, to the control. CAR from weeks 9 to 24 in the intervention group was significantly higher than that in the control group (63.9% vs. 50.5%; odds ratio [OR], 1.73; 95% confidence interval [CI], 1.24 to 2.42; P = 0.001). The CAR from weeks 9 to 52 was also higher in the intervention group than that in the control group (52.3% vs. 41.5%; OR, 1.55; 95% CI, 1.11 to 2.16; P = 0.010). No specific adverse events caused by the CASC system were reported. Augmenting standard face-to-face counseling and pharmacotherapy with a novel smartphone app, the CASC system significantly improved long-term CARs compared to standard treatment and a minimally supportive control app.

Mobile phone text messaging and app-based interventions for smoking cessation

BACKGROUND

Mobile phone-based smoking cessation support (mCessation) offers the opportunity to provide behavioural support to those who cannot or do not want face-to-face support. In addition, mCessation can be automated and therefore provided affordably even in resource-poor settings. This is an update of a Cochrane Review first published in 2006, and previously updated in 2009 and 2012.

OBJECTIVES

To determine whether mobile phone-based smoking cessation interventions increase smoking cessation rates in people who smoke.

SEARCH METHODS

For this update, we searched the Cochrane Tobacco Addiction Group's Specialised Register, along with clinicaltrials.gov and the ICTRP. The date of the most recent searches was 29 October 2018.

SELECTION CRITERIA

Participants were smokers of any age. Eligible interventions were those testing any type of predominantly mobile phone-based programme (such as text messages (or smartphone app) for smoking cessation. We included randomised controlled trials with smoking cessation outcomes reported at at least six-month follow-up.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures described in the Cochrane Handbook for Systematic Reviews of Interventions. We performed both study eligibility checks and data extraction in duplicate. We performed meta-analyses of the most stringent measures of abstinence at six months' follow-up or longer, using a Mantel-Haenszel random-effects method, pooling studies with similar interventions and similar comparators to calculate risk ratios (RR) and their corresponding 95% confidence intervals (CI). We conducted analyses including all randomised (with dropouts counted as still smoking) and complete cases only.

MAIN RESULTS

This review includes 26 studies (33,849 participants). Overall, we judged 13 studies to be at low risk of bias, three at high risk, and the remainder at unclear risk. Settings and recruitment procedures varied across studies, but most studies were conducted in high-income countries. There was moderate-certainty evidence, limited by inconsistency, that automated text messaging interventions were more effective than minimal smoking cessation support (RR 1.54, 95% CI 1.19 to 2.00; I² = 71%; 13 studies, 14,133 participants). There was also moderate-certainty evidence, limited by imprecision, that text messaging added to other smoking cessation interventions was more effective than the other smoking cessation interventions alone (RR 1.59, 95% CI 1.09 to 2.33; I² = 0%, 4 studies, 997 participants). Two studies comparing text messaging with other smoking cessation interventions, and three studies comparing high- and low-intensity messaging, did not show significant differences between groups (RR 0.92 95% CI 0.61 to 1.40; I² = 27%; 2 studies, 2238 participants; and RR 1.00, 95% CI 0.95 to 1.06; I² = 0%, 3 studies, 12,985 participants, respectively) but confidence intervals were wide in the former comparison. Five studies compared a smoking cessation smartphone app with lower-intensity smoking cessation support (either a lower-intensity app or non-app minimal support). We pooled the evidence and deemed it to be of very low certainty due to inconsistency and serious imprecision. It provided no evidence that smartphone apps improved the likelihood of smoking cessation (RR 1.00, 95% CI 0.66 to 1.52; I² = 59%; 5 studies, 3079 participants). Other smartphone apps tested differed from the apps included in the analysis, as two used contingency management and one combined text messaging with an app, and so we did not pool them. Using complete case data as opposed to using data from all participants randomised did not substantially alter the findings.

AUTHORS' CONCLUSIONS

There is moderate-certainty evidence that automated text message-based smoking cessation interventions result in greater quit rates than minimal smoking cessation support. There is moderate-certainty evidence of the benefit of text messaging interventions in addition to other smoking cessation support in comparison with that smoking cessation support alone. The evidence comparing smartphone apps with less intensive support was of very low certainty, and more randomised controlled trials are needed to test these interventions.

Competitions for smoking cessation

BACKGROUND

Competitions might encourage people to undertake and/or reinforce behaviour change, including smoking cessation. Competitions involve individuals or groups having the opportunity to win a prize following successful cessation, either through direct competition or by entry into a lottery or raffle.

OBJECTIVES

To determine whether competitions lead to higher long-term smoking quit rates. We also aimed to examine the impact on the population, the costs, and the unintended consequences of smoking cessation competitions.

SEARCH METHODS

This review has merged two previous Cochrane reviews. Here we include studies testing competitions from the reviews 'Competitions and incentives for smoking cessation' and 'Quit & Win interventions for smoking cessation'. We updated the evidence by searching the Cochrane Tobacco Addiction Group Specialized Register in June 2018.

SELECTION CRITERIA

We considered randomized controlled trials (RCTs), allocating individuals, workplaces, groups within workplaces, or communities to experimental or control conditions. We also considered controlled studies with baseline and post-intervention measures in which participants were assigned to interventions by the investigators. Participants were smokers, of any age and gender, in any setting. Eligible interventions were contests, competitions, lotteries, and raffles, to reward cessation and continuous abstinence in smoking cessation programmes.

DATA COLLECTION AND ANALYSIS

For this update, data from new studies were extracted independently by two review authors. The primary outcome measure was abstinence from smoking at least six months from the start of the intervention. We performed meta-analyses to pool study effects where suitable data were available and where the effect of the competition component could be separated from that of other intervention components, and report other findings narratively.

MAIN RESULTS

Twenty studies met our inclusion criteria. Five investigated performance-based reward, where groups of smokers competed against each other to win a prize (N = 915). The remaining 15 used performance-based eligibility, where cessation resulted in entry into a prize draw (N = 10,580). Five of these used Quit & Win contests (N = 4282), of which three were population-level interventions. Fourteen studies were RCTs, and the remainder quasi-randomized or controlled trials. Six had suitable abstinence data for a meta-analysis, which did not show evidence of effectiveness of performance-based eligibility interventions (risk ratio (RR) 1.16, 95% confidence interval (CI) 0.77 to 1.74, N = 3201, I2 = 57%). No trials that used performance-based rewards found a beneficial effect of the intervention on long-term quit rates.The three population-level Quit & Win studies found higher smoking cessation rates in the intervention group (4% to 16.9%) than the control group at long-term follow-up, but none were RCTs and all had important between-group differences in baseline characteristics. These studies suggested that fewer than one in 500 smokers would quit because of the contest.Reported unintended consequences in all sets of studies generally related to discrepancies between self-reported smoking status and biochemically-verified smoking status. More serious adverse events were not attributed to the competition intervention.Using the GRADE system we rated the overall quality of the evidence for smoking cessation as 'very low', because of the high and unclear risk of bias associated with the included studies, substantial clinical and methodological heterogeneity, and the limited population investigated.

AUTHORS' CONCLUSIONS

At present, it is impossible to draw any firm conclusions about the effectiveness, or a lack of it, of smoking cessation competitions. This is due to a lack of well-designed comparative studies. Smoking cessation competitions have not been shown to enhance long-term cessation rates. The limited evidence suggesting that population-based Quit & Win contests at local and regional level might deliver quit rates above baseline community rates has not been tested adequately using rigorous study designs. It is also unclear whether the value or frequency of possible cash reward schedules influence the success of competitions. Future studies should be designed to compensate for the substantial biases in the current evidence base.

Integrated Health Care and mHealth: A Model of Care for Refugees with Complex Health Conditions

High rates of comorbid physical and mental health conditions are documented among refugee populations. A dearth of evidence exists on the use of mHealth technologies to support integrated health care models, with interprofessional mental and physical healthcare teams, within the field of refugee health, despite the potential for mHealth technologies to reduce barriers to health care access for vulnerable populations. This conceptual article illustrates how mHealth can facilitate integrated health care models with refugees with comorbid conditions. Implications are made to support the application of mHealth technologies within integrated health care models serving at-risk refugee populations.

The contribution of participant, treatment, and outcome factors to treatment satisfaction

Treatment satisfaction, which refers to the positive appraisal of process and outcome attributes of a treatment, is a prominent indicator of quality care. Although it is known that participant, treatment, and outcome factors influence treatment satisfaction, it remains unclear which factors contribute to satisfaction with each process and outcome attribute. In this study, we examined the extent to which participant (age, gender, education, race, employment), treatment (type of therapy, method of assignment to therapy), and outcome (self-reported improvement in outcome) factors contribute to satisfaction with the process and outcome attributes of therapies for insomnia. This study consists of a secondary analysis of data obtained from a partially randomized preference trial in which persons with chronic insomnia (N = 517) were assigned to treatment randomly or by preference. Four types of behavioral therapies were included: sleep hygiene, stimulus control therapy, sleep restriction therapy, and multi-component therapy. Self-reported improvement in insomnia and satisfaction were assessed with validated measures at post-test. Multiple regression analysis was used to examine which factors influenced satisfaction with each treatment attribute. The findings showed that treatment and outcome, more so than participant, factors influenced satisfaction with the process and outcome attributes of the behavioral therapies for insomnia. Future research on satisfaction should explore the contribution of treatment (type and preference-matching) and outcome factors on satisfaction to build a better understanding of treatment attributes viewed favorably. Such understanding has the potential to inform modifying or tailoring treatments to improve their acceptance to participants and optimize their effectiveness.

Psychometric evaluation of a multi-dimensional measure of satisfaction with behavioral interventions

Treatment satisfaction is recognized as an essential aspect in the evaluation of an intervention's effectiveness, but there is no measure that provides for its comprehensive assessment with regard to behavioral interventions. Informed by a conceptualization generated from a literature review, we developed a measure that covers several domains of satisfaction with behavioral interventions. In this paper, we briefly review its conceptualization and describe the Multi-Dimensional Treatment Satisfaction Measure (MDTSM) subscales. Satisfaction refers to the appraisal of the treatment's process and outcome attributes. The MDTSM has 11 subscales assessing treatment process and outcome attributes: treatment components' suitability and utility, attitude toward treatment, desire for continued treatment use, therapist competence and interpersonal style, format and dose, perceived benefits of the health problem and everyday functioning, discomfort, and attribution of outcomes to treatment. The MDTSM was completed by persons (N = 213) in the intervention group in a large trial of a multi-component behavioral intervention for insomnia within 1 week following treatment completion. The MDTSM's subscales demonstrated internal consistency reliability (α: .65 - .93) and validity (correlated with self-reported adherence and perceived insomnia severity at post-test). The MDTSM subscales can be used to assess satisfaction with behavioral interventions and point to aspects of treatments that are viewed favorably or unfavorably.

Contingency management treatment for substance use disorders: How far has it come, and where does it need to go?

Contingency management (CM) interventions consistently improve substance abuse treatment outcomes, yet CM remains a highly controversial intervention and is rarely implemented in practice settings. This article briefly outlines the evidence base of CM and then describes 4 of the most often-cited concerns about it: philosophical, motivational, durability, and economic. Data supporting and refuting each of these issues are reviewed. The article concludes with suggestions to address these matters and other important areas for CM research and implementation, with the aims of improving uptake of this efficacious intervention in practice settings and outcomes of patients with substance use disorders. (PsycINFO Database Record

Contingency Management: New Directions and Remaining Challenges for An Evidence-Based Intervention

This article introduces the special issue on contingency management (CM), an efficacious intervention for the treatment of substance use disorders with low uptake in clinical settings that is not commensurate with evidence for efficacy. In this special issue of the Journal of Substance Abuse Treatment, we present 16 articles representing the latest research in efficacy, implementation, and technological advances related to CM. Combined, this collection of articles highlights the diverse populations, settings, and applications of CM in the treatment of substance use disorders. We conclude by highlighting directions for future research, particularly those that may increase CM's appeal and uptake in routine clinical care.