Multidimensional individualised Physical ACTivity (Mi-PACT)--a technology-enabled intervention to promote physical activity in primary care: study protocol for a randomised controlled trial

Most UK cardiovascular disease trial protocols feature criteria that exclude ethnic minority participants: a systematic review

OBJECTIVES

We systematically reviewed UK cardiovascular disease (CVD) randomized controlled trial (RCT) protocols to identify the proportion featuring eligibility criteria that may disproportionately exclude ethnic minority (EM) participants.

METHODS

We searched MEDLINE, Embase, and Cochrane Library databases, January 2014-June 2022, to identify UK CVD RCT protocols. We extracted nonclinical eligibility criteria from trial protocols and inductively categorized the trials by their language, consent, and broad (ambiguous) criteria. Findings are narratively reported.

RESULTS

Of the seventy included RCT protocols, most (87.1%; 61/70) mentioned consent within the eligibility criteria, with more than two-thirds (68.9%; 42/61) indicating a requirement for 'written' consent. Alternative consent pathways that can aid EM participation were absent. English language requirement was present in 22.9% (16/70) of the studies and 37.1% (26/70) featured broad criteria that are open to interpretation and subject to recruiter bias. Only 4.3% (3/70) protocols mentioned the provision of translation services.

CONCLUSION

Most UK CVD trial protocols feature eligibility criteria that potentially exclude EM groups. Trial eligibility criteria must be situated within a larger inclusive recruitment framework, where ethnicity is considered alongside other intersecting and disadvantaging identities.

The effects of short-term, progressive exercise training on disease activity in smouldering multiple myeloma and monoclonal gammopathy of undetermined significance: a single-arm pilot study

BACKGROUND

High levels of physical activity are associated with reduced risk of the blood cancer multiple myeloma (MM). MM is preceded by the asymptomatic stages of monoclonal gammopathy of undetermined significance (MGUS) and smouldering multiple myeloma (SMM) which are clinically managed by watchful waiting. A case study (N = 1) of a former elite athlete aged 44 years previously indicated that a multi-modal exercise programme reversed SMM disease activity. To build from this prior case study, the present pilot study firstly examined if short-term exercise training was feasible and safe for a group of MGUS and SMM patients, and secondly investigated the effects on MGUS/SMM disease activity.

METHODS

In this single-arm pilot study, N = 20 participants diagnosed with MGUS or SMM were allocated to receive a 16-week progressive exercise programme. Primary outcome measures were feasibility and safety. Secondary outcomes were pre- to post-exercise training changes to blood biomarkers of MGUS and SMM disease activity- monoclonal (M)-protein and free light chains (FLC)- plus cardiorespiratory and functional fitness, body composition, quality of life, blood immunophenotype, and blood biomarkers of inflammation.

RESULTS

Fifteen (3 MGUS and 12 SMM) participants completed the exercise programme. Adherence was 91 ± 11%. Compliance was 75 ± 25% overall, with a notable decline in compliance at intensities > 70% V̇O2PEAK. There were no serious adverse events. There were no changes to M-protein (0.0 ± 1.0 g/L, P =.903), involved FLC (+ 1.8 ± 16.8 mg/L, P =.839), or FLC difference (+ 0.2 ± 15.6 mg/L, P =.946) from pre- to post-exercise training. There were pre- to post-exercise training improvements to diastolic blood pressure (- 3 ± 5 mmHg, P =.033), sit-to-stand test performance (+ 5 ± 5 repetitions, P =.002), and energy/fatigue scores (+ 10 ± 15%, P =.026). Other secondary outcomes were unchanged.

CONCLUSIONS

A 16-week progressive exercise programme was feasible and safe, but did not reverse MGUS/SMM disease activity, contrasting a prior case study showing that five years of exercise training reversed SMM in a 44-year-old former athlete. Longer exercise interventions should be explored in a group of MGUS/SMM patients, with measurements of disease biomarkers, along with rates of disease progression (i.e., MGUS/SMM to MM).

REGISTRATION

https://www.isrctn.com/ISRCTN65527208 (14/05/2018).

Effectiveness of Computer-Tailored Health Communication in Increasing Physical Activity in People With or at Risk of Long-Term Conditions: Systematic Review and Meta-Analysis

BACKGROUND

Regular physical activity (PA) is beneficial for enhancing and sustaining both physical and mental well-being as well as for the management of preexisting conditions. Computer-tailored health communication (CTHC) has been shown to be effective in increasing PA and many other health behavior changes in the general population. However, individuals with or at risk of long-term conditions face unique barriers that may limit the applicability of CTHC interventions to this population. Few studies have focused on this cohort, providing limited evidence for the effectiveness of CTHC in promoting PA.

OBJECTIVE

This systematic review and meta-analysis aims to assess the effectiveness of CTHC in increasing PA in individuals with or at risk of long-term conditions.

METHODS

A systematic review and meta-analysis were conducted to evaluate the effect of CTHC in increasing PA in people with or at risk of long-term conditions. Hedges g was used to calculate the mean effect size. The total effect size was pooled and weighted using inverse variance. When possible, potential moderator variables were synthesized, and their effectiveness was evaluated by subgroups analysis with Q test for between-group heterogeneity Qb. Potential moderator variables included behavior change theories and models providing the fundamental logic for CTHC design, behavior change techniques and tailoring strategies to compose messages, and computer algorithms to achieve tailoring. Several methods were used to examine potential publication bias in the results, including the funnel plot, Egger test, Begg test, fail-safe N test, and trim-and-fill method.

RESULTS

In total, 24 studies were included in the systematic review for qualitative analysis and 18 studies were included in the meta-analysis. Significant small to medium effect size values were found when comparing CTHC to general health information (Hedges g=0.16; P<.001) and to no information sent to participants (Hedges g=0.29; P<.001). Half of the included studies had a low to moderate risk of bias, and the remaining studies had a moderate to high risk of bias. Although the results of the meta-analysis indicated no evidence of publication bias, caution is required when drawing definitive conclusions due to the limited number of studies in each subgroup (N≤10). Message-tailoring strategies, implementation strategies, behavior change theories and models, and behavior change techniques were synthesized from the 24 studies. No strong evidence was found from subgroup analyses on the effectiveness of using particular behavior change theories and models or from using particular message-tailoring and implementation strategies.

CONCLUSIONS

This study demonstrates that CTHC is effective in increasing PA for people with or at risk of long-term conditions, with significant small to medium effects compared with general health information or no information. Further studies are needed to guide design decisions for maximizing the effectiveness of CTHC.

Supporting Behavior Change in Sedentary Adults via Real-time Multidimensional Physical Activity Feedback: Mixed Methods Randomized Controlled Trial

BACKGROUND

Increasing physical activity (PA) behavior remains a public health priority, and wearable technology is increasingly being used to support behavior change efforts. Using wearables to capture and provide comprehensive, visually persuasive, multidimensional feedback with real-time support may be a promising way of increasing PA in inactive individuals.

OBJECTIVE

This study aims to explore whether a 6-week self-monitoring intervention using composite web-based multidimensional PA feedback with real-time daily feedback supports increased PA in adults.

METHODS

A 6-week, mixed methods, 2-armed exploratory randomized controlled trial with 6-week follow-up was used, whereby low to moderately active (PA level [PAL] <2.0) adults (mean age 51.3 years, SD 8.4 years; women 28/51, 55%) were randomly assigned to receive the self-monitoring intervention (36/51, 71%) or waiting list control (15/51, 29%). Assessment of PA across multiple health-harnessing PA dimensions (eg, PAL, weekly moderate to vigorous intensity PA, sedentary time, and steps), psychosocial cognitions (eg, behavioral regulation, barrier self-efficacy, and habit strength), and health were made at the prerandomization baseline at 6 and 12 weeks. An exploratory analysis of the mean difference and CIs was conducted using the analysis of covariance model. After the 12-week assessment, intervention participants were interviewed to explore their views on the program.

RESULTS

There were no notable differences in any PA outcome immediately after the intervention; however, at 12 weeks, moderate-to-large effects were observed with a mean difference in PAL of 0.09 (95% CI 0.02-0.15; effect size [Hedges g] 0.8), daily moderate-intensity PA of 24 (95% CI 0-45; Hedges g=0.6) minutes, weekly moderate-to-vigorous intensity PA of 195 (95% CI 58-331; Hedges g=0.8) minutes, and steps of 1545 (95% CI 581-2553; Hedges g=0.7). Descriptive analyses suggested that the differences in PA at 12 weeks were more pronounced in women and participants with lower baseline PA levels. Immediately after the intervention, there were favorable differences in autonomous motivation, controlled motivation, perceived competence for PA, and barrier self-efficacy, with the latter sustained at follow-up. Qualitative data implied that the intervention was highly informative for participants and that the real-time feedback element was particularly useful in providing tangible, day-to-day behavioral support.

CONCLUSIONS

Using wearable trackers to capture and present sophisticated multidimensional PA feedback combined with discrete real-time support may be a useful way of facilitating changes in behavior. Further investigation into the ways of optimizing the use of wearables in inactive participants and testing the efficacy of this approach via a robust study design is warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02432924; https://clinicaltrials.gov/ct2/show/NCT02432924.

Effect of novel technology-enabled multidimensional physical activity feedback in primary care patients at risk of chronic disease - the MIPACT study: a randomised controlled trial

BACKGROUND

Technological progress has enabled the provision of personalised feedback across multiple dimensions of physical activity that are important for health. Whether this multidimensional approach supports physical activity behaviour change has not yet been examined. Our objective was to examine the effectiveness of a novel digital system and app that provided multidimensional physical activity feedback combined with health trainer support in primary care patients identified as at risk of chronic disease.

METHODS

MIPACT was a parallel-group, randomised controlled trial that recruited patients at medium (≥10 and < 20%) or high (≥20%) risk of cardiovascular disease and/or type II diabetes from six primary care practices in the United Kingdom. Intervention group participants (n = 120) received personal multidimensional physical activity feedback using a customised digital system and web-app for 3 months plus five health trainer-led sessions. All participants received standardised information regarding physical activity. Control group participants (n = 84) received no further intervention. The primary outcome was device-based assessment of physical activity at 12 months.

RESULTS

Mean intervention effects were: moderate-vigorous physical activity: -1.1 (95% CI, - 17.9 to 15.7) min/day; moderate-vigorous physical activity in ≥10-min bouts: 0.2 (- 14.2 to 14.6) min/day; Physical Activity Level (PAL): 0.00 (- 0.036 to 0.054); vigorous physical activity: 1.8 (- 0.8 to 4.2) min/day; and sedentary time: 10 (- 19.3 to 39.3) min/day. For all of these outcomes, the results showed that the groups were practically equivalent and statistically ruled out meaningful positive or negative effects (>minimum clinically important difference, MCID). However, there was profound physical activity multidimensionality, and only a small proportion (5%) of patients had consistently low physical activity across all dimensions.

CONCLUSION

In patients at risk of cardiovascular disease and/or type II diabetes, MIPACT did not increase mean physical activity. Using a sophisticated multidimensional digital approach revealed enormous heterogeneity in baseline physical activity in primary care patients, and practitioners may need to screen for low physical activity across dimensions rather than rely on disease-risk algorithms that are heavily influenced by age.

TRIAL REGISTRATION

This trial is registered with the ISRCTN registry ( ISRCTN18008011 ; registration date 31 July 2013).

How do mobile health applications support behaviour changes? A scoping review of mobile health applications relating to physical activity and eating behaviours

OBJECTIVE

The objective of this review was to analyse how researchers conducting studies about mobile health applications (MHApps) effectiveness assess the conditions of this effectiveness.

STUDY DESIGN

A scoping review according to PRIMSA-ScR checklist.

METHODS

We conducted a scoping review of efficacy/effectiveness conditions in high internal validity studies assessing the efficacy of MHApps in changing physical activity behaviours and eating habits. We used the PubMed, Web of Science, SPORTDiscus and PsycINFO databases and processed the review according to the O'Malley and PRISMA-ScR recommendations. We selected studies with high internal validity methodologies (randomised controlled trials, quasi-experimental studies, systematic reviews and meta-analyses), dealing with dietary and/or physical activity behaviours; covering primary, secondary or tertiary prevention and dealing with behaviour change (uptake, maintenance). We excluded articles on MHApps relating to high-level sport and telemedicine. The process for selecting studies followed a set protocol with two authors who independently appraised the studies.

RESULTS

Twenty-two articles were finally selected and analysed. We noted that the mechanisms and techniques to support behaviour changes were poorly reported and studied. There was no explanation of how these MHApps work and how they could be transferred or not. Indeed, the main efficacy conditions reported by authors refer to practical aspects of the tools. Moreover, the issue of social inequalities was essentially reduced to access to the technology (the shrinking access divide), and literacy was poorly studied, even though it is an important consideration in digital prevention. All in all, even when they dealt with behaviours, the evaluations were tool-focused rather than intervention-focused and did not allow a comprehensive assessment of MHApps.

CONCLUSION

To understand the added value of MHApps in supporting behaviour changes, it seems important to draw on the paradigms relating to health technology assessment considering the characteristics of the technologies and on the evaluation of complex interventions considering the characteristics of prevention. This combined approach may help to clarify how these patient-focused MHApps work and is a condition for improved assessment of MHApps in terms of effectiveness, transferability and scalability.

High-intensity interval training for health benefits and care of cardiac diseases - The key to an efficient exercise protocol

Aerobic capacity, which is expressed as peak oxygen consumption (VO_2peak), is well-known to be an independent predictor of all-cause mortality and cardiovascular prognosis. This is true even for people with various coronary risk factors and cardiovascular diseases. Although exercise training is the best method to improve VO_2peak, the guidelines of most academic societies recommend 150 or 75 min of moderate- or vigorous- intensity physical activities, respectively, every week to gain health benefits. For general health and primary and secondary cardiovascular prevention, high-intensity interval training (HIIT) has been recognized as an efficient exercise protocol with short exercise sessions. Given the availability of the numerous HIIT protocols, which can be classified into aerobic HIIT and anaerobic HIIT [usually called sprint interval training (SIT)], professionals in health-related fields, including primary physicians and cardiologists, may find it confusing when trying to select an appropriate protocol for their patients. This review describes the classifications of aerobic HIIT and SIT, and their differences in terms of effects, target subjects, adaptability, working mechanisms, and safety. Understanding the HIIT protocols and adopting the correct type for each subject would lead to better improvements in VO_2peak with higher adherence and less risk.

Consumer-Based Wearable Activity Trackers Increase Physical Activity Participation: Systematic Review and Meta-Analysis

BACKGROUND

The range of benefits associated with regular physical activity participation is irrefutable. Despite the well-known benefits, physical inactivity remains one of the major contributing factors to ill-health throughout industrialized countries. Traditional lifestyle interventions such as group education or telephone counseling are effective at increasing physical activity participation; however, physical activity levels tend to decline over time. Consumer-based wearable activity trackers that allow users to objectively monitor activity levels are now widely available and may offer an alternative method for assisting individuals to remain physically active.

OBJECTIVE

This review aimed to determine the effects of interventions utilizing consumer-based wearable activity trackers on physical activity participation and sedentary behavior when compared with interventions that do not utilize activity tracker feedback.

METHODS

A systematic review was performed searching the following databases for studies that included the use of a consumer-based wearable activity tracker to improve physical activity participation: Cochrane Controlled Register of Trials, MEDLINE, PubMed, Scopus, Web of Science, Cumulative Index of Nursing and Allied Health Literature, SPORTDiscus, and Health Technology Assessments. Controlled trials of adults comparing the use of a consumer-based wearable activity tracker with other nonactivity tracker-based interventions were included. The main outcome measures were physical activity participation and sedentary behavior. All studies were assessed for risk of bias, and the Grades of Recommendation, Assessment, Development, and Evaluation system was used to rank the quality of evidence. The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement were followed. A random-effects meta-analysis was completed on the included outcome measures to estimate the treatment effect of interventions that included an activity tracker compared with a control group.

RESULTS

There was a significant increase in daily step count (standardized mean difference [SMD] 0.24; 95% CI 0.16 to 0.33; P<.001), moderate and vigorous physical activity (SMD 0.27; 95% CI 0.15 to 0.39; P<.001), and energy expenditure (SMD 0.28; 95% CI 0.03 to 0.54; P=.03) and a nonsignificant decrease in sedentary behavior (SMD -0.20; 95% CI -0.43 to 0.03; P=.08) following the intervention versus control comparator across all studies in the meta-analyses. In general, included studies were at low risk of bias, except for performance bias. Heterogeneity varied across the included meta-analyses ranging from low (I²=3%) for daily step count through to high (I²=67%) for sedentary behavior.

CONCLUSIONS

Utilizing a consumer-based wearable activity tracker as either the primary component of an intervention or as part of a broader physical activity intervention has the potential to increase physical activity participation. As the effects of physical activity interventions are often short term, the inclusion of a consumer-based wearable activity tracker may provide an effective tool to assist health professionals to provide ongoing monitoring and support.

Consumer-Based Wearable Activity Trackers Increase Physical Activity Participation: Systematic Review and Meta-Analysis

BACKGROUND

The range of benefits associated with regular physical activity participation is irrefutable. Despite the well-known benefits, physical inactivity remains one of the major contributing factors to ill-health throughout industrialized countries. Traditional lifestyle interventions such as group education or telephone counseling are effective at increasing physical activity participation; however, physical activity levels tend to decline over time. Consumer-based wearable activity trackers that allow users to objectively monitor activity levels are now widely available and may offer an alternative method for assisting individuals to remain physically active.

OBJECTIVE

This review aimed to determine the effects of interventions utilizing consumer-based wearable activity trackers on physical activity participation and sedentary behavior when compared with interventions that do not utilize activity tracker feedback.

METHODS

A systematic review was performed searching the following databases for studies that included the use of a consumer-based wearable activity tracker to improve physical activity participation: Cochrane Controlled Register of Trials, MEDLINE, PubMed, Scopus, Web of Science, Cumulative Index of Nursing and Allied Health Literature, SPORTDiscus, and Health Technology Assessments. Controlled trials of adults comparing the use of a consumer-based wearable activity tracker with other nonactivity tracker-based interventions were included. The main outcome measures were physical activity participation and sedentary behavior. All studies were assessed for risk of bias, and the Grades of Recommendation, Assessment, Development, and Evaluation system was used to rank the quality of evidence. The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement were followed. A random-effects meta-analysis was completed on the included outcome measures to estimate the treatment effect of interventions that included an activity tracker compared with a control group.

RESULTS

There was a significant increase in daily step count (standardized mean difference [SMD] 0.24; 95% CI 0.16 to 0.33; P<.001), moderate and vigorous physical activity (SMD 0.27; 95% CI 0.15 to 0.39; P<.001), and energy expenditure (SMD 0.28; 95% CI 0.03 to 0.54; P=.03) and a nonsignificant decrease in sedentary behavior (SMD -0.20; 95% CI -0.43 to 0.03; P=.08) following the intervention versus control comparator across all studies in the meta-analyses. In general, included studies were at low risk of bias, except for performance bias. Heterogeneity varied across the included meta-analyses ranging from low (I2=3%) for daily step count through to high (I2=67%) for sedentary behavior.

CONCLUSIONS

Utilizing a consumer-based wearable activity tracker as either the primary component of an intervention or as part of a broader physical activity intervention has the potential to increase physical activity participation. As the effects of physical activity interventions are often short term, the inclusion of a consumer-based wearable activity tracker may provide an effective tool to assist health professionals to provide ongoing monitoring and support.

Personal Activity Intelligence (PAI): A new standard in activity tracking for obtaining a healthy cardiorespiratory fitness level and low cardiovascular risk

Despite all the evidence of health benefits related to physical activity (PA) and cardiorespiratory fitness (CRF), low levels of PA have reached pandemic proportions, and inactivity is the fourth leading cause of death worldwide. Lack of time, and inability to self-manage are often cited as main barriers to getting adequate PA. Recently, a new personalized metric for PA tracking named Personal Activity Intelligence (PAI) was developed with the aim to make it easier to quantify how much PA per week is needed to reduce the risk of premature mortality from non-communicable diseases. PAI can be integrated in self-assessment heart rate devices and defines a weekly beneficial heart rate pattern during PA by considering the individual's sex, age, and resting and maximal heart rates. Among individuals ranging from the general population to subgroups of patients with cardiovascular disease (CVD), a PAI score ≥100 per week at baseline, an increase in PAI score, and a sustained high PAI score over time were found to delay premature death from CVD and all causes, regardless of whether or not the current PA recommendations were met. Importantly, a PAI score ≥100 at baseline, maintaining ≥100 PAIs and an increasing PAI score over time was associated with multiple years of life gained. Moreover, obtaining a weekly PAI ≥100 attenuated the deleterious association between CVD risk factor clustering and prolonged sitting time. PAI and objectively measured CRF (as indicated by VO_2peak) were positively associated in a graded fashion, and individuals with a PAI score between 100 and 150 had expected age and sex specific average VO_2peak values. A PAI score ≥100 was associated with higher VO_2peak in both men (4.1 mL·kg^-1·min^-1; 95% CI, 3.5 to 4.6) and women (2.9 mL·kg^-1·min^-1; 95% CI, 2.4 to 3.3), compared to the reference group of <100 PAI. The combined analysis of PAI, PA and VO_2peak demonstrated that a PAI score ≥100 was associated with high VO_2peak values regardless of meeting or not meeting the current PA recommendations. Collectively, these findings suggest that PAI has the potential to be a useful tool to motivate people to become and stay physically active by quantifying the amount of PA needed to produce significant health benefits.

The impact of multidimensional physical activity feedback on healthcare practitioners and patients

Background

Promotion of physical activity in primary care has had limited success. Wearable technology presents an opportunity to support healthcare practitioners (HCPs) in providing personalised feedback to their patients.

Aim

To explore the differing thoughts and feelings of both HCPs and at-risk patients provided with personalised multidimensional physical activity feedback.

Design & setting

Qualitative study with HCPs (n = 15) and patients at risk of cardiovascular disease or type 2 diabetes (n = 29), recruited from primary care.

Method

HCPs and patients wore a physical activity monitor for 7 days and were subsequently shown their personalised multidimensional feedback, including sedentary time, calorie burn, short (1-minute) or long (>10-minute) bouts of moderate-to-vigorous activity during semi-structured interviews. Transcripts were analysed thematically with comparisons made between individuals of high (n = 21) and low (n = 23) physical activity levels as to their cognitive–affective responses to their data.

Results

Personalised feedback elicited positive emotional responses for highly active participants and negative emotional responses for those with low activity. However, individuals with low activity demonstrated largely positive coping mechanisms. Some low active participants were in denial over feedback, but the majority valued it as an opportunity to think of ways to improve physical activity (cognitive reappraisal) and started forming action plans (problem-focused coping). Around half of all participants also sought to validate their feedback against peers.

Conclusion

Personalised, visual feedback elicits immediate emotional and coping responses in participants of high and low physical activity levels. Further studies should explore whether multidimensional feedback could help practitioners explore diverse ways for lifestyle change with patients.

Energy balance dynamics during short-term high-intensity functional training

CrossFit (CF; CrossFit Inc., Washington, DC, USA) is a form of high-intensity functional training that focuses on training across the entire spectrum of physical fitness. CF has been shown to improve a number of indicators of health but little information assessing energy balance exists. The purpose of the present study was to investigate energy balance during 1 week of CF training. Men and women (n = 21; mean ± SD; age, 43.5 ± 8.4 years; body mass index, 27.8 ± 4.9 kg·m−2), with ≥3 months CF experience, had body composition assessed via air displacement plethysmography before and after 1 week of CF training. Participants wore ActiHeart monitors to assess total energy expenditure (TEE), activity energy expenditure, and CF energy expenditure (CF EE). Energy intake was assessed from TEE and Δ body composition. CF EE averaged 605 ± 219 kcal per 72 ± 10 min session. Weekly CF EE was 2723 ± 986 kcal. Participants were in an energy deficit (TEE: 3674 ± 855 kcal·day−1; energy intake: 3167 ± 1401 kcal·day−1). Results of the present study indicate that CF training can account for a significant portion of daily activity energy expenditure. The weekly expenditure is within levels shown to induce clinically meaningful weight loss in overweight/obese populations.

User Models for Personalized Physical Activity Interventions: Scoping Review

Background

Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.

Objective

This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.

Methods

A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.

Results

The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.

Conclusions

This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.

Effectiveness of eHealth interventions for the promotion of physical activity in older adults: A systematic review

Regular physical activity (PA) is central to healthy ageing. However, only a minority of older adults currently meet the WHO-recommended PA levels. The aim of this systematic review is to compare the effectiveness of eHealth interventions promoting PA in older adults aged 55years and above with either no intervention or a non-eHealth intervention (review registration: PROSPERO CRD42015023875). Eight electronic databases were searched to identify experimental and quasi-experimental studies examining the effectiveness of eHealth interventions for PA promotion in adults aged 55years and above. Two authors independently selected and reviewed references, extracted data, and assessed study quality. In the search, 5771 records were retrieved, 20 studies met all inclusion criteria. Studies varied greatly in intervention mode, content, duration and assessed outcomes. Study quality ranged from poor to moderate. All interventions comprised tailored PA advice and the majority of interventions included goal setting and feedback, as well as PA tracking. Participation in eHealth interventions to promote PA led to increased levels of PA in adults aged 55years and above when compared to no intervention control groups, at least in the short term. However, the results were inconclusive regarding the question of whether eHealth interventions have a greater impact on PA behavior among older adults than non-eHealth interventions (e.g., print interventions). eHealth interventions can effectively promote PA in older adults aged 55years and above in the short-term, while evidence regarding long-term effects and the added benefit of eHealth compared to non-eHealth intervention components is still lacking.

Energy balance components in persons with paraplegia: daily variation and appropriate measurement duration

Background

Despite obesity being highly prevalent in persons with spinal cord injury (SCI), our current understanding of the interactions between energy balance components, which may contribute to this, is limited. The primary aim of this study is to identify the intra-individual variability of physical activity dimensions across days and suggest an appropriate monitoring time frame for these constructs in adults with SCI. The secondary aim is to examine these parameters with regard to energy intake and dietary macronutrient composition.

Methods

Participants [33 men and women with chronic (> 1 year post injury) paraplegia; age = 44 ± 9 years (mean ± S.D.] wore an Actiheart™ PA monitor and completed a weighed food diary for 7 consecutive days. Spearman-Brown Prophecy Formulae, based on Intraclass Correlations of .80 (acceptable reliability), were used to predict the number of days required to measure energy balance components. Linear mixed-effects analyses and magnitude-based inferences were performed for all energy intake, expenditure and physical activity dimensions. Adjustments were made for age, injury level, wear time, sex, day of the week and measurement order as fixed effects.

Results

To reliably measure energy expenditure components; 1 day [total energy expenditure (TEE)], 2 days [physical activity energy expenditure (PAEE), light-intensity activity, moderate-to-vigorous PA (MVPA)], 3 days [physical activity level (PAL)] and 4 days (sedentary behaviour) are necessary. Device wear time (P < 0.02), injury level (P < 0.04) and sex (P < 0.001) were covariates for energy expenditure components. Four and ≤24 days are required to reliably measure total energy intake (kcal) and diet macronutrient composition (%), respectively. Measurement order (from day 1–7) was a covariate for total energy intake (P = 0.01).

Conclusions

This is the first study to demonstrate the variability of energy intake and expenditure components in free-living persons with chronic (> 1 year) paraplegia and propose suitable measurement durations to achieve acceptable reliability in outcome measures. Device wear time and measurement order play a role in the quality of energy expenditure and intake data, respectively, and should be considered when designing and analysing studies of energy balance components in persons with SCI.

Trial registration

N/A

Personalized Activity Intelligence (PAI) for Prevention of Cardiovascular Disease and Promotion of Physical Activity

PURPOSE

To derive and validate a single metric of activity tracking that associates with lower risk of cardiovascular disease mortality.

METHODS

We derived an algorithm, Personalized Activity Intelligence (PAI), using the HUNT Fitness Study (n = 4631), and validated it in the general HUNT population (n = 39,298) aged 20-74 years. The PAI was divided into three sex-specific groups (≤50, 51-99, and ≥100), and the inactive group (0 PAI) was used as the referent. Hazard ratios for all-cause and cardiovascular disease mortality were estimated using Cox proportional hazard regressions.

RESULTS

After >1 million person-years of observations during a mean follow-up time of 26.2 (SD 5.9) years, there were 10,062 deaths, including 3867 deaths (2207 men and 1660 women) from cardiovascular disease. Men and women with a PAI level ≥100 had 17% (95% confidence interval [CI], 7%-27%) and 23% (95% CI, 4%-38%) reduced risk of cardiovascular disease mortality, respectively, compared with the inactive groups. Obtaining ≥100 PAI was associated with significantly lower risk for cardiovascular disease mortality in all prespecified age groups, and in participants with known cardiovascular disease risk factors (all P-trends <.01). Participants who did not obtain ≥100 PAI had increased risk of dying regardless of meeting the physical activity recommendations.

CONCLUSION

PAI may have a huge potential to motivate people to become and stay physically active, as it is an easily understandable and scientifically proven metric that could inform potential users of how much physical activity is needed to reduce the risk of premature cardiovascular disease death.

Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors

Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.

Feedback from physical activity monitors is not compatible with current recommendations: A recalibration study

Wearable devices to self-monitor physical activity have become popular with individuals and healthcare practitioners as a route to the prevention of chronic disease. It is not currently possible to reconcile feedback from these devices with activity recommendations because the guidelines refer to the amount of activity required on top of normal lifestyle activities (e.g., 150 minutes of moderate-to-vigorous intensity activity per week over-and-above normal moderate-to-vigorous lifestyle activities). The aim of this study was to recalibrate the feedback from self-monitoring. We pooled data from four studies conducted between 2006 and 2014 in patients and volunteers from the community that included both sophisticated measures of physical activity and 10-year risk for cardiovascular disease and type 2 diabetes (n=305). We determined the amount of moderate-to-vigorous intensity activity that corresponded to FAO/WHO/UNU guidance for a required PAL of 1.75 (Total Energy Expenditure/Basal Metabolic Rate). Our results show that, at the UK median PAL, total moderate-to-vigorous intensity physical activity will be around 735 minutes per week (~11% of waking time). We estimate that a 4% increase in moderate-to-vigorous intensity activity will achieve standardised guidance from FAO/WHO/UNU and will require ~1000 minutes of moderate-to-vigorous intensity activity per week. This study demonstrates that feedback from sophisticated wearable devices is incompatible with current physical activity recommendations. Without adjustment, people will erroneously form the view that they are exceeding recommendations by several fold. A more appropriate target from self-monitoring that accounts for normal moderate-to-vigorous lifestyle activities is ~1000 minutes per week, which represents ~15% of waking time.