Equity-Specific Effects of Interventions to Promote Physical Activity among Middle-Aged and Older Adults: Development of a Collaborative Equity-Specific Re-Analysis Strategy

Engagement With Remote Delivery Channels in a Physical Activity Intervention for Senior Women in the US

PURPOSE

Identify the effects of engagement with different intervention delivery channels on physical activity (PA), and the participant subgroups engaging with the different channels, among Women's Health Initiative Strong and Healthy (WHISH) PA trial participants.

DESIGN

Secondary analysis of data from WHISH, a pragmatic trial that used passive randomized consent.

SETTING

United States (remote intervention in all 50 states).

SAMPLE

18,080 U.S. women, aged 68-99 years, assigned to the WHISH PA intervention arm.

MEASURES

6 dichotomous variables operationalized engagement: Engagement with Targeted Inserts, Email (opened), Email (clicked links), Website (logging in), Website (tracking), Interactive Voice Response (IVR). PA was measured using the CHAMPS PA questionnaire.

ANALYSIS

Linear regressions evaluated effects of engagement on PA. Conditional Inference Trees identified subgroups of participants engaging with different channels based on demographic and psychosocial variables.

RESULTS

Engagement with each channel, except IVR, was associated with significantly more hours/week of PA (square root coefficients .29 - .13, P values <.001). Consistently across channels, features that identified subgroups of participants with higher engagement included younger age, and higher levels of PA and physical function. Subgroups with the highest engagement differed from those with the lowest in most participant characteristics.

CONCLUSIONS

For equitable population-level impact via large-scale remotely-delivered PA programs, it may be necessary to identify strategies to engage and target harder to reach subgroups more precisely.

CLINICAL TRIAL REGISTRATION

The WHISH trial is registered at ClinicalTrials.gov (No. NCT02425345).

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

BACKGROUND

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

OBJECTIVES

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

SEARCH METHODS

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

SELECTION CRITERIA

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

DATA COLLECTION AND ANALYSIS

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

MAIN RESULTS

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

AUTHORS' CONCLUSIONS

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

Equity-specific effects of interventions to promote physical activity among middle-aged and older adults: results from applying a novel equity-specific re-analysis strategy

BACKGROUND

Reducing inequalities in physical activity (PA) and PA-associated health outcomes is a priority for public health. Interventions to promote PA may reduce inequalities, but may also unintentionally increase them. Thus, there is a need to analyze equity-specific intervention effects. However, the potential for analyzing equity-specific effects of PA interventions has not yet been sufficiently exploited. The aim of this study was to set out a novel equity-specific re-analysis strategy tried out in an international interdisciplinary collaboration.

METHODS

The re-analysis strategy comprised harmonizing choice and definition of outcomes, exposures, socio-demographic indicators, and statistical analysis strategies across studies, as well as synthesizing results. It was applied in a collaboration of a convenience sample of eight European PA intervention studies in adults aged ≥45 years. Weekly minutes of moderate-to-vigorous PA was harmonized as outcome. Any versus no intervention was harmonized as exposure. Gender, education, income, area deprivation, and marital status were harmonized as socio-demographic indicators. Interactions between the intervention and socio-demographic indicators on moderate-to-vigorous PA were analyzed using multivariable linear regression and random-effects meta-analysis.

RESULTS

The collaborative experience shows that the novel re-analysis strategy can be applied to investigate equity-specific effects of existing PA interventions. Across our convenience sample of studies, no consistent pattern of equity-specific intervention effects was found. Pooled estimates suggested that intervention effects did not differ by gender, education, income, area deprivation, and marital status.

CONCLUSIONS

To exploit the potential for equity-specific effect analysis, we encourage future studies to apply the strategy to representative samples of existing study data. Ensuring sufficient representation of 'hard to reach' groups such as the most disadvantaged in study samples is of particular importance. This will help to extend the limited evidence required for the design and prioritization of future interventions that are most likely to reduce health inequalities.

[Evidence-based primary prevention and health promotion: methods and procedures in 5 research consortia]

Von 2014 bis 2022 erforschen die 5 deutschen Forschungsverbünde AEQUIPA, CAPITAL4HEALTH, HLCA, PartKommPlus und SMARTACT Themen der Primärprävention und Gesundheitsförderung mit dem Ziel, die Evidenzgrundlagen in diesen Bereichen weiterzuentwickeln. In diesem Beitrag wird die Arbeit der 5 Forschungsverbünde für Primärprävention und Gesundheitsförderung unter dem Aspekt der Evidenzbasierung aus der internen Perspektive vorgestellt, analysiert und diskutiert. Als orientierender Rahmen dient ein Modell der evidenzbasierten Public Health.

Die 5 Forschungsverbünde nutzen für die Evidenzgenerierung vielfältige Zugangswege bzgl. der Beteiligung nichtakademischer, zivilgesellschaftlicher Akteur*innen und Nutzer*innen. Es finden sich vielfältige Studiendesigns, die von randomisiert kontrollierten Studien und systematischen Reviews zu diversen qualitativen Designs reichen. Die Nutzung von Modellen und Theorien unterstützt die Evidenzbasierung. Über die Evidenzentwicklung hinaus legen alle Verbünde einen Schwerpunkt auf die zumindest exemplarische Implementierung des neuen Wissens.

Durch die Methodenvielfalt kann eine breit gefächerte Evidenzbasierung unter Berücksichtigung verbundspezifischer Aspekte realisiert werden. Grenzen für eine weitere systematische Stärkung der Evidenzbasierung liegen in strukturellen Rahmenbedingungen. Insbesondere die Einbindung von nichtakademischen, zivilgesellschaftlichen Akteur*innen und Nutzer*innen für die Arbeit mit schwer erreichbaren Zielgruppen kann oft nicht ausfinanziert bzw. zeitlich berücksichtigt werden. Die COVID-19-Pandemie verdeutlicht die Wichtigkeit eines flexiblen Methodenspektrums, in dem ein sinnvolles Zusammenspiel von digitalen und analogen Methoden anzustreben ist.

Equity impacts of interventions to increase physical activity among older adults: a quantitative health impact assessment

BACKGROUND

Behavioural interventions may increase social inequalities in health. This study aimed to project the equity impact of physical activity interventions that have differential effectiveness across education groups on the long-term health inequalities by education and gender among older adults in Germany.

METHODS

We created six intervention scenarios targeting the elderly population: Scenarios #1-#4 applied realistic intervention effects that varied by education (low, medium high). Under scenario #5, all older adults adapted the physical activity pattern of those with a high education. Under scenario #6, all increased their physical activity level to the recommended 300 min weekly. The number of incident ischemic heart disease, stroke and diabetes cases as well as deaths from all causes under each of these six intervention scenarios was simulated for males and females over a 10-year projection period using the DYNAMO-HIA tool. Results were compared against a reference-scenario with unchanged physical activity.

RESULTS

Under scenarios #1-#4, approximately 3589-5829 incident disease cases and 6248-10,320 deaths could be avoided among males over a 10-year projection period, as well as 4381-7163 disease cases and 6914-12,605 deaths among females. The highest reduction for males would be achieved under scenario #4, under which the intervention is most effective for those with a high education level. Scenario #4 realizes 2.7 and 2.4% of the prevented disease cases and deaths observed under scenario #6, while increasing inequalities between education groups. In females, the highest reduction would be achieved under scenario #3, under which the intervention is most effective amongst those with low levels of education. This scenario realizes 2.7 and 2.9% of the prevented disease cases and deaths under scenario #6, while decreasing inequalities between education groups. Under scenario #5, approximately 31,687 incident disease cases and 59,068 deaths could be prevented among males over a 10-year projection period, as well as 59,173 incident disease cases and 121,689 deaths among females. This translates to 14.4 and 22.2% of the prevented diseases cases among males and females under scenario #6, and 13.7 and 27.7% of the prevented deaths under scenario #6.

CONCLUSIONS

This study shows how the overall population health impact varies depending on how the intervention-induced physical activity change differs across education groups. For decision-makers, both the assessment of health impacts overall as well as within a population is relevant as interventions with the greatest population health gain might be accompanied by an unintended increase in health inequalities.

Mobile health (m‐health) smartphone interventions for overweight or obese adolescents and adults

This is a protocol for a Cochrane Review (intervention). The objectives are as follows:

To assess the effects of smartphone‐based m‐health interventions for overweight or obese adolescents and adults.