Clinical trial NCT05620888: Efficacy of a Tailored Communication Intervention Aimed At Increasing the Number of Daily Steps
Official title: Efficacy of a Tailored Communication Intervention Aimed At Increasing the Number of Daily Steps
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Physical Inactivity
Keywords: Physical Activity
Tailored communication
Behavior Change
Self Efficacy
Health Action Process Approach
Persuasive Communication
Walking
Lead sponsor: University of Milano Bicocca
Sponsor class: OTHER
Collaborators: name: Federico II University
name: University of Bergamo
First posted: 2022-11-17
Enrollment: 255
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief summary: This study aims to evaluate the efficacy of a physical activity promotion intervention focused on walking behavior. The intervention is delivered via mobile application in a sample drawn from the healthy adult population.

Detailed description: A sample of sedentary adults will be invited to participate in a 30 days intervention delivered via a mobile app to achieve the goal of 7,000 daily steps.

Before and after the intervention, some crucial variables will be evaluated for the formation of the intention to change behavior and for the transition from intention to action (attitudes, subjective norms, self-efficacy, expectations related to change, risk perception, social support, planning) to compare two models of behavioral change.

During the intervention, messages will be sent daily, and the number of steps taken daily will be monitored. The aim is to compare the effectiveness of two types of communication in promoting a positive behavioral change: non-personalized communication centered on well-being (non-tailored communication) vs. personalized communication based on the psychological characteristics evaluated before the intervention (tailored communication). The physical activity carried out over 30 days by the participants who will receive the messages (tailored and non-tailored) will be compared with the physical activity carried out by participants who will not receive any messages (control group).

Eligibility criteria: Inclusion Criteria:

* Participants from the general population, in good health and sedentary
* A level of education sufficient to understand the procedures of the study and to use a smartphone
* Having a smartphone

Exclusion Criteria:

* The participant always (or almost always) takes at least 7,000 steps a day
* The participant achieves an IPAQ score equal to or greater than 3000 MET-min / week
* The participant has symptoms or pathologies that could represent a contraindication to the physical activity proposed by the study. In particular
* Cardiovascular diseases for which physical activity is allowed only under medical supervision
* Chest pain during daily activities
* Drug treatment for cardiovascular diseases
* Severe arterial hypertension not pharmacologically controlled
* Episodes of loss of consciousness within the past 12 months
* Osteoarticular disorders that could be aggravated by a change in the level of physical activity
* Fractures of the lower limbs, vertebrae, or pelvis in the past six months
* Walking difficulty
* Respiratory insufficiency

Primary outcomes:
measure: Change from Baseline in Physical Activity
timeFrame: Baseline and 30 days
measure: Change from Baseline in Walk behavior
timeFrame: Baseline and 30 days
Secondary outcomes:
Other outcomes:
measure: Adherence to a healthy lifestyle: diet
timeFrame: Baseline
measure: Adherence to a healthy lifestyle: alcohol consumption
timeFrame: Baseline
measure: Adherence to a healthy lifestyle: smoking
timeFrame: Baseline
measure: Adherence to a healthy lifestyle: medication
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): intention to change walking behavior
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): action self-efficacy
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): health risk perception
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): planning
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): outcome expectancies
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): maintenance self-efficacy
timeFrame: Baseline
measure: Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): recovery self-efficacy
timeFrame: Baseline
measure: Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): instrumental attitude
timeFrame: Baseline
measure: Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): subjective norms
timeFrame: Baseline
measure: Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): social support
timeFrame: Baseline
measure: Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): anticipated affective reactions
timeFrame: Baseline
Locations:
city: Milan
country: Italy
facility: University of Milano-Bicocca
state: MI
Study officials:
affiliation: University of Milano Bicocca
name: Marco D'Addario, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: University of Milano Bicocca
name: Patrizia Steca, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Davis A, Sweigart R, Ellis R. A systematic review of tailored mHealth interventions for physical activity promotion among adults. Transl Behav Med. 2020 Oct 12;10(5):1221-1232. doi: 10.1093/tbm/ibz190.
citation: Martin-Martin J, Roldan-Jimenez C, De-Torres I, Muro-Culebras A, Escriche-Escuder A, Gonzalez-Sanchez M, Ruiz-Munoz M, Mayoral-Cleries F, Biro A, Tang W, Nikolova B, Salvatore A, Cuesta-Vargas AI. Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review. Front Digit Health. 2021 Jul 8;3:620383. doi: 10.3389/fdgth.2021.620383. eCollection 2021.
citation: Newsome A, Gilliard T, Phillips A, Dedrick R. Understanding the perceptions of sedentary college students' engagement in physical activity: application of the theory of planned behavior. J Am Coll Health. 2023 Dec;71(9):2813-2822. doi: 10.1080/07448481.2021.1998069. Epub 2021 Nov 17.
citation: Romeo A, Edney S, Plotnikoff R, Curtis R, Ryan J, Sanders I, Crozier A, Maher C. Can Smartphone Apps Increase Physical Activity? Systematic Review and Meta-Analysis. J Med Internet Res. 2019 Mar 19;21(3):e12053. doi: 10.2196/12053.
citation: Rowley TW, Lenz EK, Swartz AM, Miller NE, Maeda H, Strath SJ. Efficacy of an Individually Tailored, Internet-Mediated Physical Activity Intervention in Older Adults: A Randomized Controlled Trial. J Appl Gerontol. 2019 Jul;38(7):1011-1022. doi: 10.1177/0733464817735396. Epub 2017 Oct 25.
citation: Steca P, Pancani L, Cesana F, Fattirolli F, Giannattasio C, Greco A, D'Addario M, Monzani D, Cappelletti ER, Magrin ME, Miglioretti M, Sarini M, Scrignaro M, Vecchio L, Franzelli C. Changes in physical activity among coronary and hypertensive patients: A longitudinal study using the Health Action Process Approach. Psychol Health. 2017 Mar;32(3):361-380. doi: 10.1080/08870446.2016.1273353. Epub 2017 Jan 4.
citation: Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med. 2004;34(1):1-8. doi: 10.2165/00007256-200434010-00001.
citation: Zhang CQ, Zhang R, Schwarzer R, Hagger MS. A meta-analysis of the health action process approach. Health Psychol. 2019 Jul;38(7):623-637. doi: 10.1037/hea0000728. Epub 2019 Apr 11.
citation: Carfora V, Caso D, Palumbo F, Conner M. Promoting water intake. The persuasiveness of a messaging intervention based on anticipated negative affective reactions and self-monitoring. Appetite. 2018 Nov 1;130:236-246. doi: 10.1016/j.appet.2018.08.017. Epub 2018 Aug 16.
citation: Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care. 1986 Jan;24(1):67-74. doi: 10.1097/00005650-198601000-00007.
citation: Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991; 50(2): 179-211.
citation: Mannocci A, Di Thiene D, Del Cimmuto A, Masala D, Boccia A, De Vito E, La Torre G. International Physical Activity Questionnaire: validation and assessment in an Italian sample. Italian Journal of Public Health. 2012; 7(4)
citation: Schwarzer R. Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology. 2008; 57(1): 1-29