Delivering "Just-In-Time" Smoking Cessation Support Via Mobile Phones: Current Knowledge and Future Directions.

UNLABELLED: Smoking lapses early on during a quit attempt are highly predictive of failing to quit. A large proportion of these lapses are driven by cravings brought about by situational and environmental cues. Use of cognitive-behavioral lapse prevention strategies to combat cue-induced cravings is associated with a reduced risk of lapse, but evidence is lacking in how these strategies can be effectively promoted. Unlike most traditional methods of delivering behavioral support, mobile phones can in principle deliver automated support, including lapse prevention strategy recommendations, Just-In-Time (JIT) for when a smoker is most vulnerable, and prevent early lapse. JIT support can be activated by smokers themselves (user-triggered), by prespecified rules (server-triggered) or through sensors that dynamically monitor a smoker's context and trigger support when a high risk environment is sensed (context-triggered), also known as a Just-In-Time Adaptive Intervention (JITAI). However, research suggests that user-triggered JIT cessation support is seldom used and existing server-triggered JIT support is likely to lack sufficient accuracy to effectively target high-risk situations in real time. Evaluations of mobile phone cessation interventions that include user and/or server-triggered JIT support have yet to adequately assess whether this improves management of high risk situations. While context-triggered systems have the greatest potential to deliver JIT support, there are, as yet, no impact evaluations of such systems. Although it may soon be feasible to learn about and monitor a smoker's context unobtrusively using their smartphone without burdensome data entry, there are several potential advantages to involving the smoker in data collection. IMPLICATIONS: This commentary describes the current knowledge on the potential for mobile phones to deliver automated support to help smokers manage or cope with high risk environments or situations for smoking, known as JIT support. The article categorizes JIT support into three main types: user-triggered, server-triggered, and context-triggered. For each type of JIT support, a description of the evidence and their potential to effectively target specific high risk environments or situations is described. The concept of unobtrusive sensing without user data entry to inform the delivery of JIT support is finally discussed in relation to potential advantages and disadvantages for behavior change.This is the accepted manuscript. The final version is available at http://ntr.oxfordjournals.org/content/early/2016/06/15/ntr.ntw143

Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

Investigating the Temporal Relationships between Symptoms and Nebuliser Adherence in People with Cystic Fibrosis: A Series of N-of-1 Observations

Treatment adherence in adults with cystic fibrosis (CF) is poor. One of the reasons identified for lack of adherence to nebulised treatments is that patients may not experience any immediate relief in their symptoms or notice changes as a result of taking their treatment, thus many report that they do not perceive there to be consequences of non adherence. The aim of the study was to investigate the temporal relationships between symptoms and adherence to nebulised treatments in adults with CF using an N-of-1 observational design. Six participants were recruited for a six-week period during which time they completed a daily online respiratory symptom questionnaire. Adherence to treatment was measured throughout the duration of the study using an eTrack® nebuliser that logged date and time of treatments taken. Data generated from each participant was analysed separately. There were significant relationships between pain and adherence for three participants, tiredness and adherence for one participant and cough and adherence for one participant. For all of these findings, the symptom and adherence were experienced on the same day. Extending the monitoring period beyond six weeks may provide increased insight into the complex relationship between symptoms and adherence in CF

Impact of the NHS Stop Smoking Services on smoking prevalence in England:A simulation modelling evaluation

Background The English National Health Service NHS Stop Smoking Services (SSS), established in 2001, were the first such services in the world. An appropriate evaluation of the SSS has national and international significance. This modelling study sought to evaluate the impact of the SSS on changes in smoking prevalence in England. Methods A discrete time state-transition model was developed to simulate changes in smoking status among the adult population in England during 2001-2016. Input parameters were based on data from national statistics, population representative surveys and published literature. The main outcome was the percentage point reduction in smoking prevalence attributable to the SSS. Results Smoking prevalence was reduced by 10.8 % in absolute terms during 2001-2016 in England, and 15.3 % of the reduction could be attributable to the SSS. The percentage point reduction in smoking prevalence each year was on average 0.72%, and 0.11 % could be attributable to the SSS. The proportion of SSS supported quit attempts increased from 5.5 % in 2001, to as high as 18.9 % in 2011, and then reduced to 8.2 % in 2016. Quit attempts with SSS support had a higher success rate than those without SSS support (15.1% vs 11.3%). Smoking prevalence in England continued to decline after the SSS was much reduced from 2013 onwards. Conclusions Approximately 15% of the percentage point reduction in smoking prevalence during 2001-2016 in England may be attributable to the NHS SSS, although uncertainty remains regarding the actual impact of the formal smoking cessation services

A parent-oriented alcohol prevention program "Effekt" had no impact on adolescents' alcohol use: Findings from a cluster-randomized controlled trial in Estonia.

AIM: To evaluate the effectiveness of a universal parent-oriented alcohol prevention program ("Effekt") in Estonia. The main objective of the program was to delay and reduce adolescents' alcohol consumption by maintaining parental restrictive attitudes towards adolescents' alcohol use over time. METHODS: A matched-pair cluster randomized controlled trial with a three-year assessment period (baseline (T1), 18-months (T2) and 30-months (T3) follow-ups) was undertaken in 2012-2015 among 985 fifth grade adolescents and 790 parents in sixty-six schools (34 intervention, 32 control). The primary outcome measure was adolescents' alcohol use initiation. Secondary outcome measures were lifetime drunkenness and alcohol use in the past year. Intermediate outcomes were restrictive parental attitudes towards adolescents' alcohol use reported by parents and perceived restrictive parental attitudes and parental alcohol supply reported by adolescents. RESULTS: There were no significant differences in adolescents' alcohol use initiation, lifetime drunkenness, alcohol use in the past year, parental alcohol supply, and adolescent's perception of parental restrictive attitudes between intervention and control school participants at T2 and T3. There were significant differences in parental attitudes - the odds of having restrictive attitudes were 2.05 (95% confidence interval (CI) = 1.32-3.17) times higher at T2 and 1.92 (95% CI = 1.31-2.83) times higher at T3 in the intervention group than in the control group. CONCLUSIONS: The Estonian version of the "Effekt" program had a positive effect on parental attitudes, but it did not succeed in delaying or reducing adolescents' alcohol consumption.Estonian National Institute for Health Development, Archimedes Foundatio

A starter kit for undertaking n-of-1 trials

The aim of this article is to provide readers who have not yet undertaken n-of-1 or within-subject experimental studies with a general overview of the methodology from a health psychology perspective and to provide some tools to give readers the opportunity to give it a go themselves

Future smoking prevalence by socioeconomic status in England: a computational modelling study

Background: The difference in smoking across socioeconomic groups is a major cause of health inequality. This study projected future smoking prevalence by socioeconomic status, and revealed what is needed to achieve the tobacco-free ambition (TFA) by 2030 in England. Methods: Using data from multiple sources, the adult (≥18 years) population in England was separated into subgroups by smoking and highest educational qualification (HEQ). A discrete time state-transition model was used to project future smoking prevalence by HEQ deterministically and stochastically. Results: In a status quo scenario, smoking prevalence in England is projected to be 10.8% (95% uncertainty interval: 9.1% to 12.9%) by 2022, 7.8% (5.5% to 11.0%) by 2030 and 6.0% (3.7% to 9.6%) by 2040. The absolute difference in smoking rate between low and high HEQ is reduced from 12.2% in 2016 to 7.9% by 2030, but the relative inequality (low/high HEQ ratio) is increased from 2.48 in 2016 to 3.06 by 2030. When applying 2016 initiation/relapse rates, achievement of the TFA target requires no changes to future cessation rates among adults with high qualifications, but increased rates of 37% and 149%, respectively, in adults with intermediate and low qualifications. Conclusions: If the current trends continue, smoking prevalence in England is projected to decline in the future, but with substantial differences across socioeconomic groups. Absolute inequalities in smoking are likely to decline and relative inequalities in smoking are likely to increase in future. The achievement of England’s TFA will require the reduction of both absolute and relative inequalities in smoking by socioeconomic status

A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity.

BACKGROUND: Progress in mobile health (mHealth) technology has enabled the design of just-in-time adaptive interventions (JITAIs). We define JITAIs as having three features: behavioural support that directly corresponds to a need in real-time; content or timing of support is adapted or tailored according to input collected by the system since support was initiated; support is system-triggered. We conducted a systematic review of JITAIs for physical activity to identify their features, feasibility, acceptability and effectiveness. METHODS: We searched Scopus, Medline, Embase, PsycINFO, Web of Science, DBLP, ACM Digital Library, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov and the ISRCTN register using terms related to physical activity, mHealth interventions and JITAIs. We included primary studies of any design reporting data about JITAIs, irrespective of population, age and setting. Outcomes included physical activity, engagement, uptake, feasibility and acceptability. Paper screening and data extraction were independently validated. Synthesis was narrative. We used the mHealth Evidence Reporting and Assessment checklist to assess quality of intervention descriptions. RESULTS: We screened 2200 titles, 840 abstracts, 169 full-text papers, and included 19 papers reporting 14 unique JITAIs, including six randomised studies. Five JITAIs targeted both physical activity and sedentary behaviour, five sedentary behaviour only, and four physical activity only. JITAIs prompted breaks following sedentary periods and/or suggested physical activities during opportunistic moments, typically over three to four weeks. Feasibility challenges related to the technology, sensor reliability and timeliness of just-in-time messages. Overall, participants found JITAIs acceptable. We found mixed evidence for intervention effects on behaviour, but no study was sufficiently powered to detect any effects. Common behaviour change techniques were goal setting (behaviour), prompts/cues, feedback on behaviour and action planning. Five studies reported a theory-base. We found lack of evidence about cost-effectiveness, uptake, reach, impact on health inequalities, and sustained engagement. CONCLUSIONS: Research into JITAIs to increase physical activity and reduce sedentary behaviour is in its early stages. Consistent use and a shared definition of the term 'JITAI' will aid evidence synthesis. We recommend robust evaluation of theory and evidence-based JITAIs in representative populations. Decision makers and health professionals need to be cautious in signposting patients to JITAIs until such evidence is available, although they are unlikely to cause health-related harm. REFERENCE: PROSPERO 2017 CRD42017070849.The work was undertaken by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration (MR/K023187/), is gratefully acknowledged

Interventions to increase adherence to medications for tobacco dependence.

BACKGROUND: Pharmacological treatments for tobacco dependence, such as nicotine replacement therapy (NRT), have been shown to be safe and effective interventions for smoking cessation. Higher levels of adherence to these medications increase the likelihood of sustained smoking cessation, but many smokers use them at a lower dose and for less time than is optimal. It is important to determine the effectiveness of interventions designed specifically to increase medication adherence. Such interventions may address motivation to use medication, such as influencing beliefs about the value of taking medications, or provide support to overcome problems with maintaining adherence. OBJECTIVES: To assess the effectiveness of interventions aiming to increase adherence to medications for smoking cessation on medication adherence and smoking abstinence compared with a control group typically receiving standard care. SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group Specialized Register, and clinical trial registries (ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform) to the 3 September 2018. We also conducted forward and backward citation searches. SELECTION CRITERIA: Randomised, cluster-randomised or quasi-randomised studies in which adults using active pharmacological treatment for smoking cessation were allocated to an intervention arm where there was a principal focus on increasing adherence to medications for tobacco dependence, or a control arm providing standard care. Dependent on setting, standard care may have comprised minimal support or varying degrees of behavioural support. Included studies used a measure that allowed assessment of the degree of medication adherence. DATA COLLECTION AND ANALYSIS: Two authors independently screened studies for eligibility, extracted data for included studies and assessed risk of bias. For continuous outcome measures, we calculated effect sizes as standardised mean differences (SMDs). For dichotomous outcome measures, we calculated effect sizes as risk ratios (RRs). In meta-analyses for adherence outcomes, we combined dichotomous and continuous data using the generic inverse variance method and reported pooled effect sizes as SMDs; for abstinence outcomes, we reported and pooled dichotomous outcomes. We obtained pooled effect sizes with 95% confidence intervals (CIs) using random-effects models. We conducted subgroup analyses to assess whether the primary focus of the adherence treatment ('practicalities' versus 'perceptions' versus both), the delivery approach (participant versus clinician-centred) or the medication type were associated with effectiveness. MAIN RESULTS: We identified two new studies, giving a total of 10 studies, involving 3655 participants. The medication adherence interventions studied were all provided in addition to standard behavioural support.They typically provided further information on the rationale for, and emphasised the importance of, adherence to medication or supported the development of strategies to overcome problems with maintaining adherence (or both). Seven studies targeted adherence to NRT, two to bupropion and one to varenicline. Most studies were judged to be at high or unclear risk of bias, with four of these studies judged at high risk of attrition or detection bias. Only one study was judged to be at low risk of bias.Meta-analysis of all 10 included studies (12 comparisons) provided moderate-certainty evidence that adherence interventions led to small improvements in adherence (i.e. the mean amount of medication consumed; SMD 0.10, 95% CI 0.03 to 0.18; I² = 6%; n = 3655), limited by risk of bias. Subgroup analyses for the primary outcome identified no significant subgroup effects, with effect sizes for subgroups imprecisely estimated. However, there was a very weak indication that interventions focused on the 'practicalities' of adhering to treatment (i.e. capabilities, resources, levels of support or skills) may be effective (SMD 0.21, 95% CI 0.03 to 0.38; I² = 39%; n = 1752), whereas interventions focused on treatment 'perceptions' (i.e. beliefs, cognitions, concerns and preferences; SMD 0.10, 95% CI -0.03 to 0.24; I² = 0%; n = 839) or on both (SMD 0.04, 95% CI -0.08 to 0.16; I² = 0%; n = 1064), may not be effective. Participant-centred interventions may be effective (SMD 0.12, 95% CI 0.02 to 0.23; I² = 20%; n = 2791), whereas those that are clinician-centred may not (SMD 0.09, 95% CI -0.05 to 0.23; I² = 0%; n = 864).Five studies assessed short-term smoking abstinence (five comparisons), while an overlapping set of five studies (seven comparisons) assessed long-term smoking abstinence of six months or more. Meta-analyses resulted in low-certainty evidence that adherence interventions may slightly increase short-term smoking cessation rates (RR 1.08, 95% CI 0.96 to 1.21; I² = 0%; n = 1795) and long-term smoking cessation rates (RR 1.16, 95% CI 0.96 to 1.40; I² = 48%; n = 3593). In both cases, the evidence was limited by risk of bias and imprecision, with CIs encompassing minimal harm as well as moderate benefit, and a high likelihood that further evidence will change the estimate of the effect. There was no evidence that interventions to increase adherence to medication led to any adverse events. Studies did not report on factors plausibly associated with increases in adherence, such as self-efficacy, understanding of and attitudes toward treatment, and motivation and intentions to quit. AUTHORS' CONCLUSIONS: In people who are stopping smoking and receiving behavioural support, there is moderate-certainty evidence that enhanced behavioural support focusing on adherence to smoking cessation medications can modestly improve adherence. There is only low-certainty evidence that this may slightly improve the likelihood of cessation in the shorter or longer-term. Interventions to increase adherence can aim to address the practicalities of taking medication, change perceptions about medication, such as reasons to take it or concerns about doing so, or both. However, there is currently insufficient evidence to confirm which approach is more effective. There is no evidence on whether such interventions are effective for people who are stopping smoking without standard behavioural support.NIHR U