CClinicalTrials.gg
RecruitingNCT05620888Updated Dec 9, 2024

Efficacy of a Tailored Communication Intervention Aimed At Increasing the Number of Daily Steps

An interventional study of Walk behavior change: Tailored messages (TM) and Walk behavior change: Non tailored messages (NTM) in Physical Inactivity, sponsored by University of Milano Bicocca. Recruiting at 1 site in Italy. Open to participants aged 18 Years to 70 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2024-12-09.

Sponsored by University of Milano Bicocca · Not applicable, Interventional, and Prevention

From the registry’s dates

  • Started Oct 2022; still recruiting 4 years later.
Phase
Not applicable
Study type
Interventional
Enrollment
255
Allocation
Randomized
Ages
18 Years to 70 Years
Sex
All
01

Study 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.

Read the 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).

02

Conditions studied

  • Physical Inactivity

Keywords

  • Physical Activity
  • Tailored communication
  • Behavior Change
  • Self Efficacy
  • Health Action Process Approach
  • Persuasive Communication
  • Walking
03

In context

Lead sponsor

University of Milano Bicocca is the lead sponsor of 124 studies on the registry; 48 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 70 Years
Sexes eligible
All
Accepts healthy volunteers
Yes

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

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
05

Study design

Phase
Not applicable
Primary purpose
Prevention
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Single (Participant)
Enrollment
255 participants (estimated)

Study arms

  • Experimental
    Tailored messages (TM)

    Participants assigned to this arm receive a daily tailored message on the benefits of taking at least 7000 steps daily. Tailoring concerns change-related expectations, risk perception, planning, retention capacity, resilience, and coping skills and is based on the responses provided by participants at baseline evaluation. In addition, they receive a daily request to declare the number of steps taken (walking self-monitoring).In particular, every evening, the mobile application sends a message to the participants requesting to enter the number of steps taken during the day in a dedicated app section.

    Behavioral: Walk behavior change: Tailored messages (TM)

  • Experimental
    Non tailored messages (NTM)

    Participants assigned to this arm receive a daily non-tailored message on the emotional benefits of taking at least 7000 steps daily. In addition, they receive a daily request to declare the number of steps taken (walking self-monitoring). In particular, every evening, the mobile application sends a message to the participants requesting to enter the number of steps taken during the day in a dedicated app section.

    Behavioral: Walk behavior change: Non tailored messages (NTM)

  • No intervention
    No messages (NM)

    Participants assigned to this arm receive a daily request to declare the number of steps taken (walking self-monitoring). In particular, every evening, the mobile application sends a message to the participants requesting to enter the number of steps taken during the day in a dedicated app section.

Interventions

  • BehavioralWalk behavior change: Tailored messages (TM)

    Every afternoon at the same time, the mobile application sends a message to the participants of the TM arm. The message is tailored based on the answers provided to the pre-intervention questionnaire. An example message is: "you think you are not able to walk regularly when your morale is low: do not give up because physical activity is also good for the mood!" The intervention is provided for 30 days.

  • BehavioralWalk behavior change: Non tailored messages (NTM)

    Every afternoon at the same time, the mobile application sends a message to the participants of the NTM arm. The message concerns the emotional well-being resulting from the performance of the physical activity and is not tailored. An example message is: "walking regularly in the fresh air improves your mood." The intervention is provided for 30 days.

06

What researchers measure

Primary outcomes

  1. Change from Baseline in Physical Activity

    Physical activity is assessed with the International Physical Activity Questionnaire (IPAQ; Mannocci et al., 2010). The scale comprises seven items on Physical Activity providing information about time spent walking, moderate and vigorous intensity, and sedentary activity. The elements are structured to provide separate scores for walking, moderate and vigorous intensity activity, and a combined total score to describe the overall activity level. Data collected with IPAQ are reported as a continuous measure and reported as MET-median minutes.

    Time frame: Baseline and 30 days

  2. Change from Baseline in Walk behavior

    Walk behavior is self-monitored daily. Each evening, participants receive a message and enter the number of steps taken in a specific app section based on the data reported on the smartwatch or the smartphone's native app. The mean number of steps at the intervention's beginning and the end is then calculated. These two measures are compared to verify whether a statistically significant increase in daily steps is observed over time.

    Time frame: Baseline and 30 days

Other outcomes

  1. Adherence to a healthy lifestyle: diet

    Regarding adherence to a healthy lifestyle, participants report the frequency of consumption of 17 different food types through a 7-point Likert scale. The items were taken from a survey of the National Institute of Statistics (https://www.istat.it/it/archivio/91926). We will classify each behavior as adequate (score = 1) or inadequate (score = 0), following international and national guidelines. We will add up the scores obtained for specific foods similar to the MedDietScore scale (Trichopoulou et al., 2003), in order to build a diet adequacy index for use in subsequent analyses. The higher the score, the higher the adherence to guidelines.

    Time frame: Baseline

  2. Adherence to a healthy lifestyle: alcohol consumption

    Participants report the frequency of consumption of alcoholic beverages during the last month through a 6-point Likert scale (1 = almost every day; 6 = never). The higher the score, the higher the adherence to guidelines.

    Time frame: Baseline

  3. Adherence to a healthy lifestyle: smoking

    Participants report if they smoke, and the frequency of smoking through a 6-point Likert scale (1 = every day; 6 = never). The higher the score, the higher the adherence to guidelines.

    Time frame: Baseline

  4. Adherence to a healthy lifestyle: medication

    Medication adherence is evaluated using the brief Italian version of the Morisky Green Levine Scale (MGLS), a questionnaire consisting of 4 questions with dichotomous answers (yes or no). Each behavior is classified as adequate (score = 1) or inadequate (score = 0). The item responses are added up. The higher the score, the higher the medication adherence. A total score of \<2 is indicative of poor adherence.

    Time frame: Baseline

  5. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): intention to change walking behavior

    Participants indicate how much they intend to walk regularly (take at least 7,000 steps per day at a moderate speed) through a single item on a 7-point Likert scale, where 1 = totally disagree, 7 = totally agree. The higher the score, the higher the intention to walk regularly.

    Time frame: Baseline

  6. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): action self-efficacy

    Participants indicate confidence in their abilities to walk regularly through a single item on a 5-point Likert scale, where 1 = not capable and 5 = fully capable. The higher the score, the higher the action self-efficacy.

    Time frame: Baseline

  7. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): health risk perception

    Participants indicate how exposed they feel to health risks (six items) concerning their current unhealthy behavior (i.e., how little they walk) on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score is calculated as the mean of the six items' scores. The higher the score, the higher the health risk perception.

    Time frame: Baseline

  8. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): planning

    Participants indicate if they have a detailed plan concerning six concrete aspects of achieving the goal of 7,000 steps per day (for example, when and where to walk) on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score is calculated as the mean of the six items' scores. The higher the score, the higher the planning.

    Time frame: Baseline

  9. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): outcome expectancies

    Participants indicate their positive (3 items) and negative (3 items) expectancies about the health, emotional and social effects of taking at least 7,000 steps a day on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score of each variable (positive expectancies and negative expectancies) is calculated as the mean of the three items' scores. The higher the score, the higher the expectancies.

    Time frame: Baseline

  10. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): maintenance self-efficacy

    Participants indicate confidence in their ability to maintain the new healthier behavior despite obstacles and difficulties through ten items on a 7-point Likert scale, where 1 = in no way, 7 = very much. The score is calculated as the mean of the ten items' scores. The higher the score, the higher the maintenance self-efficacy.

    Time frame: Baseline

  11. Predictors of behavioral change according to the Health Action Process Approach (HAPA, Schwarzer, 2008; Steca et al., 2017): recovery self-efficacy

    Participants indicate confidence in their ability to regain healthy behavior if a lapse occurs through a single item on a 5-point Likert scale, where 1 = not capable and 5 = fully capable. The higher the score, the higher the maintenance self-efficacy.

    Time frame: Baseline

  12. Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): instrumental attitude

    This variable is evaluated using a ten-item semantic differential on a 7-point scale, ranging from 1 (e.g., useless) to 7 (e.g., useful). Participants read each pair of adjectives and check a box more or less close to the adjective they feel is best suited to describe what it would mean to reach the goal of 7,000 steps daily. The score is calculated as the mean of the ten items' scores. The higher the score, the more positive the attitude.

    Time frame: Baseline

  13. Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): subjective norms

    Participants indicate how significant others judge regular walking as important (subjective norms or the perceived social pressure concerning walking behavior). This variable is measured through five items on a 7-point Likert scale, where 1 = totally disagree, 7 = totally agree. The score is calculated as the mean of the five items' scores. The higher the score, the stronger the subjective norms.

    Time frame: Baseline

  14. Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): social support

    Participants indicate how much support/approval they would receive from their partner, family, friends, and walking group/associations on a 5-point Likert scale, where 1 = no support and 5 = much support. The score is calculated as the mean of the four items' scores. The higher the score, the greater the perceived social support.

    Time frame: Baseline

  15. Predictors of behavioral change according to the Theory of Planned Behavior (TPB; Ajzen, 1991; Canfora et al., 2018): anticipated affective reactions

    Participants indicate to what extent they would experience positive affective reactions (in case of reaching the goal of 7,000 steps per day - 3 items) or negative affective reactions (in case of failure to reach the goal - 3 items) on a 7-point Likert scale, where 1 = totally disagree, 7 = totally agree. The score of each variable (positive affective reactions and negative affective reactions) is calculated as the mean of the three items' scores. The higher the score, the stronger the affective reaction.

    Time frame: Baseline

07

Study locations

1 of 1 sites recruiting
08

References and documents

Publications

  • 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. PubMed 33044542 ↗
  • 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. PubMed 34713097 ↗
  • 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. PubMed 34788584 ↗
  • 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. PubMed 30888321 ↗
  • 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. PubMed 29165018 ↗
  • 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. PubMed 28049344 ↗
  • 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. PubMed 14715035 ↗
  • 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. PubMed 30973747 ↗
  • 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. PubMed 30121311 ↗
  • 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. PubMed 3945130 ↗
  • Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991; 50(2): 179-211.
  • 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)
  • 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

Individual participant data

Plan to share: No

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Dec 9, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT05620888
Lead sponsor
University of Milano Bicocca
Collaborators
Federico II University, University of Bergamo
Responsible party
Marco D'Addario (Associate Professor, University of Milano Bicocca) — Principal investigator
First posted
Nov 17, 2022
Start date
Oct 3, 2022
Primary completion
Sep 30, 2024
Completion
Jun 2025 (estimated)
Last update
Dec 9, 2024

Study contacts

Marco D'Addario, PhD
Contact
marco.daddario@unimib.it
0039 02 6448 3824
Roberta Adorni, PhD
Contact
roberta.adorni1@unimib.it
Marco D'Addario, PhD
principal investigator · University of Milano Bicocca
Patrizia Steca, PhD
principal investigator · University of Milano Bicocca

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Interested in this study?

Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.

Contact study team

Follow this study

Get an email when the registry record changes — status, dates, results — or when someone posts here.

Sign in to follow

Discussion

Questions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.

Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.

Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.

Start the discussion